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  1. .gitattributes +2 -0
  2. 3_epc/displacements/group_6/errs.txt +0 -0
  3. 3_epc/displacements/group_6/nerrs.txt +0 -0
  4. 3_epc/displacements/group_6/nscf.in +261 -0
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+ 0.833333333333 0.666666666667 0.666666666667 4.62962963e-03
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+ 0.833333333333 0.666666666667 0.833333333333 4.62962963e-03
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+ 0.833333333333 0.833333333333 0.000000000000 4.62962963e-03
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+ 0.833333333333 0.833333333333 0.833333333333 4.62962963e-03
252
+
253
+ CELL_PARAMETERS angstrom
254
+ 0.00000000000000 1.78350000000000 1.78350000000000
255
+ 1.78350000000000 0.00000000000000 1.78350000000000
256
+ 1.78350000000000 1.78350000000000 0.00000000000000
257
+
258
+ ATOMIC_POSITIONS crystal
259
+ C 0.0000000000 0.0000000000 -0.0002098036
260
+ C 0.2500000000 0.2500000000 0.2500000000
261
+
3_epc/displacements/group_6/nscf.out ADDED
The diff for this file is too large to render. See raw diff
 
3_epc/displacements/group_6/pw2bgw.in ADDED
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1
+ &input_pw2bgw
2
+ prefix = 'scf'
3
+ outdir = './tmp/'
4
+ real_or_complex = 2
5
+ wfng_flag = .false.
6
+ wfng_file = 'WFN'
7
+ wfng_kgrid = .true.
8
+ wfng_nk1 = 6
9
+ wfng_nk2 = 6
10
+ wfng_nk3 = 6
11
+ wfng_dk1 = 0.0
12
+ wfng_dk2 = 0.0
13
+ wfng_dk3 = 0.0
14
+ rhog_flag = .false.
15
+ vxcg_flag = .false.
16
+ vscg_flag = .true.
17
+ vscg_file = 'VSC'
18
+ vkbg_flag = .false.
19
+ /
3_epc/displacements/group_6/pw2bgw.out ADDED
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1
+
2
+ Program PW2BGW v.7.2 starts on 19Feb2026 at 20:58:53
3
+
4
+ This program is part of the open-source Quantum ESPRESSO suite
5
+ for quantum simulation of materials; please cite
6
+ "P. Giannozzi et al., J. Phys.:Condens. Matter 21 395502 (2009);
7
+ "P. Giannozzi et al., J. Phys.:Condens. Matter 29 465901 (2017);
8
+ "P. Giannozzi et al., J. Chem. Phys. 152 154105 (2020);
9
+ URL http://www.quantum-espresso.org",
10
+ in publications or presentations arising from this work. More details at
11
+ http://www.quantum-espresso.org/quote
12
+
13
+ Parallel version (MPI), running on 8 processors
14
+
15
+ MPI processes distributed on 1 nodes
16
+ R & G space division: proc/nbgrp/npool/nimage = 8
17
+ 1124 MiB available memory on the printing compute node when the environment starts
18
+
19
+
20
+ Reading xml data from directory:
21
+
22
+ ./tmp/scf.save/
23
+
24
+ IMPORTANT: XC functional enforced from input :
25
+ Exchange-correlation= PBE
26
+ ( 1 4 3 4 0 0 0)
27
+ Any further DFT definition will be discarded
28
+ Please, verify this is what you really want
29
+
30
+
31
+ Parallelization info
32
+ --------------------
33
+ sticks: dense smooth PW G-vecs: dense smooth PW
34
+ Min 47 47 16 613 613 129
35
+ Max 48 48 18 615 615 130
36
+ Sum 379 379 139 4909 4909 1037
37
+
38
+ Using Slab Decomposition
39
+
40
+ Reading collected, re-writing distributed wavefunctions
41
+
42
+ NLCC is present
43
+
44
+ call write_vscg
45
+ done write_vscg
46
+
47
+
48
+ write_vscg : 0.00s CPU 0.00s WALL ( 1 calls)
49
+
50
+ PW2BGW : 0.09s CPU 0.11s WALL
51
+
52
+
53
+ This run was terminated on: 20:58:53 19Feb2026
54
+
55
+ =------------------------------------------------------------------------------=
56
+ JOB DONE.
57
+ =------------------------------------------------------------------------------=
3_epc/displacements/group_6/reconstruction/aohamiltonian/element.dat ADDED
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3_epc/displacements/group_6/reconstruction/aohamiltonian/orbital_types.dat ADDED
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1
+ 0 0 1 1 2
2
+ 0 0 1 1 2
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3_epc/displacements/group_6/reconstruction/aohamiltonian/rh.h5 ADDED
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3_epc/displacements/group_6/reconstruction/aohamiltonian/rlat.dat ADDED
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1
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2
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3_epc/displacements/group_6/reconstruction/calc.py ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ from HPRO import PW2AOkernel
2
+ kernel = PW2AOkernel(
3
+ lcao_interface='siesta',
4
+ lcaodata_root='/home/apolyukhin/scripts/ml/diamond-qe/aobasis',
5
+ hrdata_interface='qe-bgw',
6
+ vscdir='/home/apolyukhin/scripts/ml/diamond-qe/diamond_epc/displacements/group_6/tmp/VSC',
7
+ upfdir='/home/apolyukhin/scripts/ml/diamond-qe/pseudos',
8
+ ecutwfn=30
9
+ )
10
+ kernel.run_pw2ao_rs('./aohamiltonian')
3_epc/displacements/group_6/reconstruction/hpro.log ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ ==============================================================================
3
+ Program HPRO
4
+ Author: Xiaoxun Gong (xiaoxun.gong@gmail.com)
5
+ ==============================================================================
6
+
7
+ Structure information:
8
+ Primitive lattice vectors (angstrom):
9
+ a = ( 0.0000000 1.7835000 1.7835000)
10
+ b = ( 1.7835000 0.0000000 1.7835000)
11
+ c = ( 1.7835000 1.7835000 0.0000000)
12
+ Atomic species and numbers in unit cell: C: 2.
13
+
14
+ Atomic orbital basis:
15
+ Format: siesta
16
+ Element C:
17
+ Orbital 1: l = 0, cutoff = 4.493 a.u., norm = 1.000
18
+ Orbital 2: l = 0, cutoff = 4.502 a.u., norm = 1.000
19
+ Orbital 3: l = 1, cutoff = 5.468 a.u., norm = 1.000
20
+ Orbital 4: l = 1, cutoff = 5.479 a.u., norm = 1.000
21
+ Orbital 5: l = 2, cutoff = 5.446 a.u., norm = 1.000
22
+
23
+ Real space grid dimensions: ( 24 24 24)
24
+
25
+ Pseudopotential projectors:
26
+ Format: qe
27
+ Element C:
28
+ Orbital 1: l = 0, cutoff = 1.310 a.u., norm = 1.000
29
+ Orbital 2: l = 0, cutoff = 1.310 a.u., norm = 1.000
30
+ Orbital 3: l = 1, cutoff = 1.310 a.u., norm = 1.000
31
+ Orbital 4: l = 1, cutoff = 1.310 a.u., norm = 1.000
32
+
33
+ IO done, total wall time = 0:00:00
34
+
35
+ ===============================================
36
+ Reconstructing PW Hamiltonian to AOs in real space
37
+ ===============================================
38
+
39
+ Calculating overlap
40
+
41
+ Writing overlap matrices to disk
42
+
43
+ Constructing Hamiltonian operator with 148 blocks
44
+ 10%|████ | 15/148 [00:01<00:09, 13.36it/s]
45
+ 20%|████████ | 30/148 [00:02<00:09, 12.78it/s]
46
+ 30%|████████████▏ | 45/148 [00:03<00:08, 12.72it/s]
47
+ 41%|████████████████▏ | 60/148 [00:05<00:08, 10.86it/s]
48
+ 51%|████████████████████▎ | 75/148 [00:06<00:06, 11.93it/s]
49
+ 61%|████████████████████████▎ | 90/148 [00:07<00:05, 11.35it/s]
50
+ 71%|████████████████████████████▍ | 105/148 [00:09<00:03, 11.15it/s]
51
+ 81%|████████████████████████████████▍ | 120/148 [00:10<00:02, 11.27it/s]
52
+ 91%|████████████████████████████████████▍ | 135/148 [00:12<00:01, 9.54it/s]
53
+ 100%|████████████████████████████████████████| 148/148 [00:13<00:00, 10.95it/s]
54
+ Done, elapsed time: 13.5s.
55
+
56
+ Writing Hamiltonian matrices to disk
57
+
58
+ Job done, total wall time = 0:00:15
59
+
3_epc/displacements/group_6/scf.in ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ &CONTROL
2
+ calculation = 'scf'
3
+ verbosity = 'high'
4
+ tstress = .false.
5
+ tprnfor = .true.
6
+ outdir = './tmp/'
7
+ prefix = 'scf'
8
+ pseudo_dir = '/home/apolyukhin/scripts/ml/diamond-qe/pseudos/'
9
+ /
10
+ &SYSTEM
11
+ ibrav = 0
12
+ ecutwfc = 60
13
+ nosym = .true.
14
+ noinv = .true.
15
+ ntyp = 1
16
+ nat = 2
17
+ /
18
+ &ELECTRONS
19
+ electron_maxstep = 1000
20
+ conv_thr = 1e-13
21
+ mixing_mode = 'plain'
22
+ mixing_beta = 0.7
23
+ diagonalization = 'david'
24
+ /
25
+ &IONS
26
+ /
27
+ &CELL
28
+ /
29
+
30
+ ATOMIC_SPECIES
31
+ C 12.011 C.upf
32
+
33
+ K_POINTS automatic
34
+ 6 6 6 0 0 0
35
+
36
+ CELL_PARAMETERS angstrom
37
+ 0.00000000000000 1.78350000000000 1.78350000000000
38
+ 1.78350000000000 0.00000000000000 1.78350000000000
39
+ 1.78350000000000 1.78350000000000 0.00000000000000
40
+
41
+ ATOMIC_POSITIONS crystal
42
+ C 0.0000000000 0.0000000000 -0.0002098036
43
+ C 0.2500000000 0.2500000000 0.2500000000
44
+
3_epc/displacements/group_6/scf.out ADDED
@@ -0,0 +1,2372 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ Program PWSCF v.7.2 starts on 19Feb2026 at 20:38:49
3
+
4
+ This program is part of the open-source Quantum ESPRESSO suite
5
+ for quantum simulation of materials; please cite
6
+ "P. Giannozzi et al., J. Phys.:Condens. Matter 21 395502 (2009);
7
+ "P. Giannozzi et al., J. Phys.:Condens. Matter 29 465901 (2017);
8
+ "P. Giannozzi et al., J. Chem. Phys. 152 154105 (2020);
9
+ URL http://www.quantum-espresso.org",
10
+ in publications or presentations arising from this work. More details at
11
+ http://www.quantum-espresso.org/quote
12
+
13
+ Parallel version (MPI), running on 8 processors
14
+
15
+ MPI processes distributed on 1 nodes
16
+ 810 MiB available memory on the printing compute node when the environment starts
17
+
18
+ Reading input from scf.in
19
+ Warning: card &CELL ignored
20
+ Warning: card / ignored
21
+
22
+ Current dimensions of program PWSCF are:
23
+ Max number of different atomic species (ntypx) = 10
24
+ Max number of k-points (npk) = 40000
25
+ Max angular momentum in pseudopotentials (lmaxx) = 4
26
+
27
+ R & G space division: proc/nbgrp/npool/nimage = 8
28
+ Subspace diagonalization in iterative solution of the eigenvalue problem:
29
+ a serial algorithm will be used
30
+
31
+
32
+ Parallelization info
33
+ --------------------
34
+ sticks: dense smooth PW G-vecs: dense smooth PW
35
+ Min 47 47 15 613 613 117
36
+ Max 48 48 16 615 615 121
37
+ Sum 379 379 121 4909 4909 941
38
+
39
+ Using Slab Decomposition
40
+
41
+
42
+
43
+ bravais-lattice index = 0
44
+ lattice parameter (alat) = 4.7664 a.u.
45
+ unit-cell volume = 76.5678 (a.u.)^3
46
+ number of atoms/cell = 2
47
+ number of atomic types = 1
48
+ number of electrons = 8.00
49
+ number of Kohn-Sham states= 4
50
+ kinetic-energy cutoff = 60.0000 Ry
51
+ charge density cutoff = 240.0000 Ry
52
+ scf convergence threshold = 1.0E-13
53
+ mixing beta = 0.7000
54
+ number of iterations used = 8 plain mixing
55
+ Exchange-correlation= PBE
56
+ ( 1 4 3 4 0 0 0)
57
+
58
+ celldm(1)= 4.766362 celldm(2)= 0.000000 celldm(3)= 0.000000
59
+ celldm(4)= 0.000000 celldm(5)= 0.000000 celldm(6)= 0.000000
60
+
61
+ crystal axes: (cart. coord. in units of alat)
62
+ a(1) = ( 0.000000 0.707107 0.707107 )
63
+ a(2) = ( 0.707107 0.000000 0.707107 )
64
+ a(3) = ( 0.707107 0.707107 0.000000 )
65
+
66
+ reciprocal axes: (cart. coord. in units 2 pi/alat)
67
+ b(1) = ( -0.707107 0.707107 0.707107 )
68
+ b(2) = ( 0.707107 -0.707107 0.707107 )
69
+ b(3) = ( 0.707107 0.707107 -0.707107 )
70
+
71
+
72
+ PseudoPot. # 1 for C read from file:
73
+ /home/apolyukhin/scripts/ml/diamond-qe/pseudos/C.upf
74
+ MD5 check sum: 34a24e64c0a39f27c6c36b90a16ac686
75
+ Pseudo is Norm-conserving + core correction, Zval = 4.0
76
+ Generated using ONCVPSP code by D. R. Hamann
77
+ Using radial grid of 1248 points, 4 beta functions with:
78
+ l(1) = 0
79
+ l(2) = 0
80
+ l(3) = 1
81
+ l(4) = 1
82
+
83
+ atomic species valence mass pseudopotential
84
+ C 4.00 12.01100 C ( 1.00)
85
+
86
+ No symmetry found
87
+
88
+
89
+ s frac. trans.
90
+
91
+ isym = 1 identity
92
+
93
+ cryst. s( 1) = ( 1 0 0 )
94
+ ( 0 1 0 )
95
+ ( 0 0 1 )
96
+
97
+ cart. s( 1) = ( 1.0000000 0.0000000 0.0000000 )
98
+ ( 0.0000000 1.0000000 0.0000000 )
99
+ ( 0.0000000 0.0000000 1.0000000 )
100
+
101
+
102
+ Cartesian axes
103
+
104
+ site n. atom positions (alat units)
105
+ 1 C tau( 1) = ( -0.0001484 -0.0001484 0.0000000 )
106
+ 2 C tau( 2) = ( 0.3535534 0.3535534 0.3535534 )
107
+
108
+ Crystallographic axes
109
+
110
+ site n. atom positions (cryst. coord.)
111
+ 1 C tau( 1) = ( 0.0000000 0.0000000 -0.0002098 )
112
+ 2 C tau( 2) = ( 0.2500000 0.2500000 0.2500000 )
113
+
114
+ number of k points= 216
115
+ cart. coord. in units 2pi/alat
116
+ k( 1) = ( 0.0000000 0.0000000 0.0000000), wk = 0.0092593
117
+ k( 2) = ( 0.1178511 0.1178511 -0.1178511), wk = 0.0092593
118
+ k( 3) = ( 0.2357023 0.2357023 -0.2357023), wk = 0.0092593
119
+ k( 4) = ( -0.3535534 -0.3535534 0.3535534), wk = 0.0092593
120
+ k( 5) = ( -0.2357023 -0.2357023 0.2357023), wk = 0.0092593
121
+ k( 6) = ( -0.1178511 -0.1178511 0.1178511), wk = 0.0092593
122
+ k( 7) = ( 0.1178511 -0.1178511 0.1178511), wk = 0.0092593
123
+ k( 8) = ( 0.2357023 0.0000000 0.0000000), wk = 0.0092593
124
+ k( 9) = ( 0.3535534 0.1178511 -0.1178511), wk = 0.0092593
125
+ k( 10) = ( -0.2357023 -0.4714045 0.4714045), wk = 0.0092593
126
+ k( 11) = ( -0.1178511 -0.3535534 0.3535534), wk = 0.0092593
127
+ k( 12) = ( 0.0000000 -0.2357023 0.2357023), wk = 0.0092593
128
+ k( 13) = ( 0.2357023 -0.2357023 0.2357023), wk = 0.0092593
129
+ k( 14) = ( 0.3535534 -0.1178511 0.1178511), wk = 0.0092593
130
+ k( 15) = ( 0.4714045 0.0000000 0.0000000), wk = 0.0092593
131
+ k( 16) = ( -0.1178511 -0.5892557 0.5892557), wk = 0.0092593
132
+ k( 17) = ( -0.0000000 -0.4714045 0.4714045), wk = 0.0092593
133
+ k( 18) = ( 0.1178511 -0.3535534 0.3535534), wk = 0.0092593
134
+ k( 19) = ( -0.3535534 0.3535534 -0.3535534), wk = 0.0092593
135
+ k( 20) = ( -0.2357023 0.4714045 -0.4714045), wk = 0.0092593
136
+ k( 21) = ( -0.1178511 0.5892557 -0.5892557), wk = 0.0092593
137
+ k( 22) = ( -0.7071068 0.0000000 0.0000000), wk = 0.0092593
138
+ k( 23) = ( -0.5892557 0.1178511 -0.1178511), wk = 0.0092593
139
+ k( 24) = ( -0.4714045 0.2357023 -0.2357023), wk = 0.0092593
140
+ k( 25) = ( -0.2357023 0.2357023 -0.2357023), wk = 0.0092593
141
+ k( 26) = ( -0.1178511 0.3535534 -0.3535534), wk = 0.0092593
142
+ k( 27) = ( -0.0000000 0.4714045 -0.4714045), wk = 0.0092593
143
+ k( 28) = ( -0.5892557 -0.1178511 0.1178511), wk = 0.0092593
144
+ k( 29) = ( -0.4714045 0.0000000 0.0000000), wk = 0.0092593
145
+ k( 30) = ( -0.3535534 0.1178511 -0.1178511), wk = 0.0092593
146
+ k( 31) = ( -0.1178511 0.1178511 -0.1178511), wk = 0.0092593
147
+ k( 32) = ( 0.0000000 0.2357023 -0.2357023), wk = 0.0092593
148
+ k( 33) = ( 0.1178511 0.3535534 -0.3535534), wk = 0.0092593
149
+ k( 34) = ( -0.4714045 -0.2357023 0.2357023), wk = 0.0092593
150
+ k( 35) = ( -0.3535534 -0.1178511 0.1178511), wk = 0.0092593
151
+ k( 36) = ( -0.2357023 0.0000000 0.0000000), wk = 0.0092593
152
+ k( 37) = ( -0.1178511 0.1178511 0.1178511), wk = 0.0092593
153
+ k( 38) = ( 0.0000000 0.2357023 0.0000000), wk = 0.0092593
154
+ k( 39) = ( 0.1178511 0.3535534 -0.1178511), wk = 0.0092593
155
+ k( 40) = ( -0.4714045 -0.2357023 0.4714045), wk = 0.0092593
156
+ k( 41) = ( -0.3535534 -0.1178511 0.3535534), wk = 0.0092593
157
+ k( 42) = ( -0.2357023 0.0000000 0.2357023), wk = 0.0092593
158
+ k( 43) = ( 0.0000000 0.0000000 0.2357023), wk = 0.0092593
159
+ k( 44) = ( 0.1178511 0.1178511 0.1178511), wk = 0.0092593
160
+ k( 45) = ( 0.2357023 0.2357023 0.0000000), wk = 0.0092593
161
+ k( 46) = ( -0.3535534 -0.3535534 0.5892557), wk = 0.0092593
162
+ k( 47) = ( -0.2357023 -0.2357023 0.4714045), wk = 0.0092593
163
+ k( 48) = ( -0.1178511 -0.1178511 0.3535534), wk = 0.0092593
164
+ k( 49) = ( 0.1178511 -0.1178511 0.3535534), wk = 0.0092593
165
+ k( 50) = ( 0.2357023 0.0000000 0.2357023), wk = 0.0092593
166
+ k( 51) = ( 0.3535534 0.1178511 0.1178511), wk = 0.0092593
167
+ k( 52) = ( -0.2357023 -0.4714045 0.7071068), wk = 0.0092593
168
+ k( 53) = ( -0.1178511 -0.3535534 0.5892557), wk = 0.0092593
169
+ k( 54) = ( 0.0000000 -0.2357023 0.4714045), wk = 0.0092593
170
+ k( 55) = ( -0.4714045 0.4714045 -0.2357023), wk = 0.0092593
171
+ k( 56) = ( -0.3535534 0.5892557 -0.3535534), wk = 0.0092593
172
+ k( 57) = ( -0.2357023 0.7071068 -0.4714045), wk = 0.0092593
173
+ k( 58) = ( -0.8249579 0.1178511 0.1178511), wk = 0.0092593
174
+ k( 59) = ( -0.7071068 0.2357023 0.0000000), wk = 0.0092593
175
+ k( 60) = ( -0.5892557 0.3535534 -0.1178511), wk = 0.0092593
176
+ k( 61) = ( -0.3535534 0.3535534 -0.1178511), wk = 0.0092593
177
+ k( 62) = ( -0.2357023 0.4714045 -0.2357023), wk = 0.0092593
178
+ k( 63) = ( -0.1178511 0.5892557 -0.3535534), wk = 0.0092593
179
+ k( 64) = ( -0.7071068 0.0000000 0.2357023), wk = 0.0092593
180
+ k( 65) = ( -0.5892557 0.1178511 0.1178511), wk = 0.0092593
181
+ k( 66) = ( -0.4714045 0.2357023 -0.0000000), wk = 0.0092593
182
+ k( 67) = ( -0.2357023 0.2357023 0.0000000), wk = 0.0092593
183
+ k( 68) = ( -0.1178511 0.3535534 -0.1178511), wk = 0.0092593
184
+ k( 69) = ( 0.0000000 0.4714045 -0.2357023), wk = 0.0092593
185
+ k( 70) = ( -0.5892557 -0.1178511 0.3535534), wk = 0.0092593
186
+ k( 71) = ( -0.4714045 -0.0000000 0.2357023), wk = 0.0092593
187
+ k( 72) = ( -0.3535534 0.1178511 0.1178511), wk = 0.0092593
188
+ k( 73) = ( -0.2357023 0.2357023 0.2357023), wk = 0.0092593
189
+ k( 74) = ( -0.1178511 0.3535534 0.1178511), wk = 0.0092593
190
+ k( 75) = ( 0.0000000 0.4714045 0.0000000), wk = 0.0092593
191
+ k( 76) = ( -0.5892557 -0.1178511 0.5892557), wk = 0.0092593
192
+ k( 77) = ( -0.4714045 -0.0000000 0.4714045), wk = 0.0092593
193
+ k( 78) = ( -0.3535534 0.1178511 0.3535534), wk = 0.0092593
194
+ k( 79) = ( -0.1178511 0.1178511 0.3535534), wk = 0.0092593
195
+ k( 80) = ( 0.0000000 0.2357023 0.2357023), wk = 0.0092593
196
+ k( 81) = ( 0.1178511 0.3535534 0.1178511), wk = 0.0092593
197
+ k( 82) = ( -0.4714045 -0.2357023 0.7071068), wk = 0.0092593
198
+ k( 83) = ( -0.3535534 -0.1178511 0.5892557), wk = 0.0092593
199
+ k( 84) = ( -0.2357023 0.0000000 0.4714045), wk = 0.0092593
200
+ k( 85) = ( 0.0000000 0.0000000 0.4714045), wk = 0.0092593
201
+ k( 86) = ( 0.1178511 0.1178511 0.3535534), wk = 0.0092593
202
+ k( 87) = ( 0.2357023 0.2357023 0.2357023), wk = 0.0092593
203
+ k( 88) = ( -0.3535534 -0.3535534 0.8249579), wk = 0.0092593
204
+ k( 89) = ( -0.2357023 -0.2357023 0.7071068), wk = 0.0092593
205
+ k( 90) = ( -0.1178511 -0.1178511 0.5892557), wk = 0.0092593
206
+ k( 91) = ( -0.5892557 0.5892557 -0.1178511), wk = 0.0092593
207
+ k( 92) = ( -0.4714045 0.7071068 -0.2357023), wk = 0.0092593
208
+ k( 93) = ( -0.3535534 0.8249579 -0.3535534), wk = 0.0092593
209
+ k( 94) = ( -0.9428090 0.2357023 0.2357023), wk = 0.0092593
210
+ k( 95) = ( -0.8249579 0.3535534 0.1178511), wk = 0.0092593
211
+ k( 96) = ( -0.7071068 0.4714045 -0.0000000), wk = 0.0092593
212
+ k( 97) = ( -0.4714045 0.4714045 -0.0000000), wk = 0.0092593
213
+ k( 98) = ( -0.3535534 0.5892557 -0.1178511), wk = 0.0092593
214
+ k( 99) = ( -0.2357023 0.7071068 -0.2357023), wk = 0.0092593
215
+ k( 100) = ( -0.8249579 0.1178511 0.3535534), wk = 0.0092593
216
+ k( 101) = ( -0.7071068 0.2357023 0.2357023), wk = 0.0092593
217
+ k( 102) = ( -0.5892557 0.3535534 0.1178511), wk = 0.0092593
218
+ k( 103) = ( -0.3535534 0.3535534 0.1178511), wk = 0.0092593
219
+ k( 104) = ( -0.2357023 0.4714045 0.0000000), wk = 0.0092593
220
+ k( 105) = ( -0.1178511 0.5892557 -0.1178511), wk = 0.0092593
221
+ k( 106) = ( -0.7071068 -0.0000000 0.4714045), wk = 0.0092593
222
+ k( 107) = ( -0.5892557 0.1178511 0.3535534), wk = 0.0092593
223
+ k( 108) = ( -0.4714045 0.2357023 0.2357023), wk = 0.0092593
224
+ k( 109) = ( 0.3535534 -0.3535534 -0.3535534), wk = 0.0092593
225
+ k( 110) = ( 0.4714045 -0.2357023 -0.4714045), wk = 0.0092593
226
+ k( 111) = ( 0.5892557 -0.1178511 -0.5892557), wk = 0.0092593
227
+ k( 112) = ( 0.0000000 -0.7071068 0.0000000), wk = 0.0092593
228
+ k( 113) = ( 0.1178511 -0.5892557 -0.1178511), wk = 0.0092593
229
+ k( 114) = ( 0.2357023 -0.4714045 -0.2357023), wk = 0.0092593
230
+ k( 115) = ( 0.4714045 -0.4714045 -0.2357023), wk = 0.0092593
231
+ k( 116) = ( 0.5892557 -0.3535534 -0.3535534), wk = 0.0092593
232
+ k( 117) = ( 0.7071068 -0.2357023 -0.4714045), wk = 0.0092593
233
+ k( 118) = ( 0.1178511 -0.8249579 0.1178511), wk = 0.0092593
234
+ k( 119) = ( 0.2357023 -0.7071068 0.0000000), wk = 0.0092593
235
+ k( 120) = ( 0.3535534 -0.5892557 -0.1178511), wk = 0.0092593
236
+ k( 121) = ( 0.5892557 -0.5892557 -0.1178511), wk = 0.0092593
237
+ k( 122) = ( 0.7071068 -0.4714045 -0.2357023), wk = 0.0092593
238
+ k( 123) = ( 0.8249579 -0.3535534 -0.3535534), wk = 0.0092593
239
+ k( 124) = ( 0.2357023 -0.9428090 0.2357023), wk = 0.0092593
240
+ k( 125) = ( 0.3535534 -0.8249579 0.1178511), wk = 0.0092593
241
+ k( 126) = ( 0.4714045 -0.7071068 -0.0000000), wk = 0.0092593
242
+ k( 127) = ( 0.0000000 0.0000000 -0.7071068), wk = 0.0092593
243
+ k( 128) = ( 0.1178511 0.1178511 -0.8249579), wk = 0.0092593
244
+ k( 129) = ( 0.2357023 0.2357023 -0.9428090), wk = 0.0092593
245
+ k( 130) = ( -0.3535534 -0.3535534 -0.3535534), wk = 0.0092593
246
+ k( 131) = ( -0.2357023 -0.2357023 -0.4714045), wk = 0.0092593
247
+ k( 132) = ( -0.1178511 -0.1178511 -0.5892557), wk = 0.0092593
248
+ k( 133) = ( 0.1178511 -0.1178511 -0.5892557), wk = 0.0092593
249
+ k( 134) = ( 0.2357023 0.0000000 -0.7071068), wk = 0.0092593
250
+ k( 135) = ( 0.3535534 0.1178511 -0.8249579), wk = 0.0092593
251
+ k( 136) = ( -0.2357023 -0.4714045 -0.2357023), wk = 0.0092593
252
+ k( 137) = ( -0.1178511 -0.3535534 -0.3535534), wk = 0.0092593
253
+ k( 138) = ( -0.0000000 -0.2357023 -0.4714045), wk = 0.0092593
254
+ k( 139) = ( 0.2357023 -0.2357023 -0.4714045), wk = 0.0092593
255
+ k( 140) = ( 0.3535534 -0.1178511 -0.5892557), wk = 0.0092593
256
+ k( 141) = ( 0.4714045 -0.0000000 -0.7071068), wk = 0.0092593
257
+ k( 142) = ( -0.1178511 -0.5892557 -0.1178511), wk = 0.0092593
258
+ k( 143) = ( 0.0000000 -0.4714045 -0.2357023), wk = 0.0092593
259
+ k( 144) = ( 0.1178511 -0.3535534 -0.3535534), wk = 0.0092593
260
+ k( 145) = ( 0.2357023 -0.2357023 -0.2357023), wk = 0.0092593
261
+ k( 146) = ( 0.3535534 -0.1178511 -0.3535534), wk = 0.0092593
262
+ k( 147) = ( 0.4714045 -0.0000000 -0.4714045), wk = 0.0092593
263
+ k( 148) = ( -0.1178511 -0.5892557 0.1178511), wk = 0.0092593
264
+ k( 149) = ( 0.0000000 -0.4714045 0.0000000), wk = 0.0092593
265
+ k( 150) = ( 0.1178511 -0.3535534 -0.1178511), wk = 0.0092593
266
+ k( 151) = ( 0.3535534 -0.3535534 -0.1178511), wk = 0.0092593
267
+ k( 152) = ( 0.4714045 -0.2357023 -0.2357023), wk = 0.0092593
268
+ k( 153) = ( 0.5892557 -0.1178511 -0.3535534), wk = 0.0092593
269
+ k( 154) = ( 0.0000000 -0.7071068 0.2357023), wk = 0.0092593
270
+ k( 155) = ( 0.1178511 -0.5892557 0.1178511), wk = 0.0092593
271
+ k( 156) = ( 0.2357023 -0.4714045 -0.0000000), wk = 0.0092593
272
+ k( 157) = ( 0.4714045 -0.4714045 -0.0000000), wk = 0.0092593
273
+ k( 158) = ( 0.5892557 -0.3535534 -0.1178511), wk = 0.0092593
274
+ k( 159) = ( 0.7071068 -0.2357023 -0.2357023), wk = 0.0092593
275
+ k( 160) = ( 0.1178511 -0.8249579 0.3535534), wk = 0.0092593
276
+ k( 161) = ( 0.2357023 -0.7071068 0.2357023), wk = 0.0092593
277
+ k( 162) = ( 0.3535534 -0.5892557 0.1178511), wk = 0.0092593
278
+ k( 163) = ( -0.1178511 0.1178511 -0.5892557), wk = 0.0092593
279
+ k( 164) = ( 0.0000000 0.2357023 -0.7071068), wk = 0.0092593
280
+ k( 165) = ( 0.1178511 0.3535534 -0.8249579), wk = 0.0092593
281
+ k( 166) = ( -0.4714045 -0.2357023 -0.2357023), wk = 0.0092593
282
+ k( 167) = ( -0.3535534 -0.1178511 -0.3535534), wk = 0.0092593
283
+ k( 168) = ( -0.2357023 -0.0000000 -0.4714045), wk = 0.0092593
284
+ k( 169) = ( 0.0000000 0.0000000 -0.4714045), wk = 0.0092593
285
+ k( 170) = ( 0.1178511 0.1178511 -0.5892557), wk = 0.0092593
286
+ k( 171) = ( 0.2357023 0.2357023 -0.7071068), wk = 0.0092593
287
+ k( 172) = ( -0.3535534 -0.3535534 -0.1178511), wk = 0.0092593
288
+ k( 173) = ( -0.2357023 -0.2357023 -0.2357023), wk = 0.0092593
289
+ k( 174) = ( -0.1178511 -0.1178511 -0.3535534), wk = 0.0092593
290
+ k( 175) = ( 0.1178511 -0.1178511 -0.3535534), wk = 0.0092593
291
+ k( 176) = ( 0.2357023 -0.0000000 -0.4714045), wk = 0.0092593
292
+ k( 177) = ( 0.3535534 0.1178511 -0.5892557), wk = 0.0092593
293
+ k( 178) = ( -0.2357023 -0.4714045 0.0000000), wk = 0.0092593
294
+ k( 179) = ( -0.1178511 -0.3535534 -0.1178511), wk = 0.0092593
295
+ k( 180) = ( 0.0000000 -0.2357023 -0.2357023), wk = 0.0092593
296
+ k( 181) = ( 0.1178511 -0.1178511 -0.1178511), wk = 0.0092593
297
+ k( 182) = ( 0.2357023 0.0000000 -0.2357023), wk = 0.0092593
298
+ k( 183) = ( 0.3535534 0.1178511 -0.3535534), wk = 0.0092593
299
+ k( 184) = ( -0.2357023 -0.4714045 0.2357023), wk = 0.0092593
300
+ k( 185) = ( -0.1178511 -0.3535534 0.1178511), wk = 0.0092593
301
+ k( 186) = ( 0.0000000 -0.2357023 0.0000000), wk = 0.0092593
302
+ k( 187) = ( 0.2357023 -0.2357023 0.0000000), wk = 0.0092593
303
+ k( 188) = ( 0.3535534 -0.1178511 -0.1178511), wk = 0.0092593
304
+ k( 189) = ( 0.4714045 0.0000000 -0.2357023), wk = 0.0092593
305
+ k( 190) = ( -0.1178511 -0.5892557 0.3535534), wk = 0.0092593
306
+ k( 191) = ( -0.0000000 -0.4714045 0.2357023), wk = 0.0092593
307
+ k( 192) = ( 0.1178511 -0.3535534 0.1178511), wk = 0.0092593
308
+ k( 193) = ( 0.3535534 -0.3535534 0.1178511), wk = 0.0092593
309
+ k( 194) = ( 0.4714045 -0.2357023 0.0000000), wk = 0.0092593
310
+ k( 195) = ( 0.5892557 -0.1178511 -0.1178511), wk = 0.0092593
311
+ k( 196) = ( -0.0000000 -0.7071068 0.4714045), wk = 0.0092593
312
+ k( 197) = ( 0.1178511 -0.5892557 0.3535534), wk = 0.0092593
313
+ k( 198) = ( 0.2357023 -0.4714045 0.2357023), wk = 0.0092593
314
+ k( 199) = ( -0.2357023 0.2357023 -0.4714045), wk = 0.0092593
315
+ k( 200) = ( -0.1178511 0.3535534 -0.5892557), wk = 0.0092593
316
+ k( 201) = ( -0.0000000 0.4714045 -0.7071068), wk = 0.0092593
317
+ k( 202) = ( -0.5892557 -0.1178511 -0.1178511), wk = 0.0092593
318
+ k( 203) = ( -0.4714045 0.0000000 -0.2357023), wk = 0.0092593
319
+ k( 204) = ( -0.3535534 0.1178511 -0.3535534), wk = 0.0092593
320
+ k( 205) = ( -0.1178511 0.1178511 -0.3535534), wk = 0.0092593
321
+ k( 206) = ( -0.0000000 0.2357023 -0.4714045), wk = 0.0092593
322
+ k( 207) = ( 0.1178511 0.3535534 -0.5892557), wk = 0.0092593
323
+ k( 208) = ( -0.4714045 -0.2357023 0.0000000), wk = 0.0092593
324
+ k( 209) = ( -0.3535534 -0.1178511 -0.1178511), wk = 0.0092593
325
+ k( 210) = ( -0.2357023 0.0000000 -0.2357023), wk = 0.0092593
326
+ k( 211) = ( 0.0000000 0.0000000 -0.2357023), wk = 0.0092593
327
+ k( 212) = ( 0.1178511 0.1178511 -0.3535534), wk = 0.0092593
328
+ k( 213) = ( 0.2357023 0.2357023 -0.4714045), wk = 0.0092593
329
+ k( 214) = ( -0.3535534 -0.3535534 0.1178511), wk = 0.0092593
330
+ k( 215) = ( -0.2357023 -0.2357023 0.0000000), wk = 0.0092593
331
+ k( 216) = ( -0.1178511 -0.1178511 -0.1178511), wk = 0.0092593
332
+
333
+ cryst. coord.
334
+ k( 1) = ( 0.0000000 0.0000000 0.0000000), wk = 0.0092593
335
+ k( 2) = ( 0.0000000 0.0000000 0.1666667), wk = 0.0092593
336
+ k( 3) = ( 0.0000000 0.0000000 0.3333333), wk = 0.0092593
337
+ k( 4) = ( 0.0000000 0.0000000 -0.5000000), wk = 0.0092593
338
+ k( 5) = ( 0.0000000 0.0000000 -0.3333333), wk = 0.0092593
339
+ k( 6) = ( 0.0000000 0.0000000 -0.1666667), wk = 0.0092593
340
+ k( 7) = ( 0.0000000 0.1666667 0.0000000), wk = 0.0092593
341
+ k( 8) = ( 0.0000000 0.1666667 0.1666667), wk = 0.0092593
342
+ k( 9) = ( 0.0000000 0.1666667 0.3333333), wk = 0.0092593
343
+ k( 10) = ( 0.0000000 0.1666667 -0.5000000), wk = 0.0092593
344
+ k( 11) = ( 0.0000000 0.1666667 -0.3333333), wk = 0.0092593
345
+ k( 12) = ( 0.0000000 0.1666667 -0.1666667), wk = 0.0092593
346
+ k( 13) = ( 0.0000000 0.3333333 0.0000000), wk = 0.0092593
347
+ k( 14) = ( 0.0000000 0.3333333 0.1666667), wk = 0.0092593
348
+ k( 15) = ( 0.0000000 0.3333333 0.3333333), wk = 0.0092593
349
+ k( 16) = ( 0.0000000 0.3333333 -0.5000000), wk = 0.0092593
350
+ k( 17) = ( 0.0000000 0.3333333 -0.3333333), wk = 0.0092593
351
+ k( 18) = ( 0.0000000 0.3333333 -0.1666667), wk = 0.0092593
352
+ k( 19) = ( 0.0000000 -0.5000000 0.0000000), wk = 0.0092593
353
+ k( 20) = ( 0.0000000 -0.5000000 0.1666667), wk = 0.0092593
354
+ k( 21) = ( 0.0000000 -0.5000000 0.3333333), wk = 0.0092593
355
+ k( 22) = ( 0.0000000 -0.5000000 -0.5000000), wk = 0.0092593
356
+ k( 23) = ( 0.0000000 -0.5000000 -0.3333333), wk = 0.0092593
357
+ k( 24) = ( 0.0000000 -0.5000000 -0.1666667), wk = 0.0092593
358
+ k( 25) = ( 0.0000000 -0.3333333 0.0000000), wk = 0.0092593
359
+ k( 26) = ( 0.0000000 -0.3333333 0.1666667), wk = 0.0092593
360
+ k( 27) = ( 0.0000000 -0.3333333 0.3333333), wk = 0.0092593
361
+ k( 28) = ( 0.0000000 -0.3333333 -0.5000000), wk = 0.0092593
362
+ k( 29) = ( 0.0000000 -0.3333333 -0.3333333), wk = 0.0092593
363
+ k( 30) = ( 0.0000000 -0.3333333 -0.1666667), wk = 0.0092593
364
+ k( 31) = ( 0.0000000 -0.1666667 0.0000000), wk = 0.0092593
365
+ k( 32) = ( 0.0000000 -0.1666667 0.1666667), wk = 0.0092593
366
+ k( 33) = ( 0.0000000 -0.1666667 0.3333333), wk = 0.0092593
367
+ k( 34) = ( 0.0000000 -0.1666667 -0.5000000), wk = 0.0092593
368
+ k( 35) = ( 0.0000000 -0.1666667 -0.3333333), wk = 0.0092593
369
+ k( 36) = ( 0.0000000 -0.1666667 -0.1666667), wk = 0.0092593
370
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371
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372
+ k( 39) = ( 0.1666667 0.0000000 0.3333333), wk = 0.0092593
373
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374
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375
+ k( 42) = ( 0.1666667 0.0000000 -0.1666667), wk = 0.0092593
376
+ k( 43) = ( 0.1666667 0.1666667 0.0000000), wk = 0.0092593
377
+ k( 44) = ( 0.1666667 0.1666667 0.1666667), wk = 0.0092593
378
+ k( 45) = ( 0.1666667 0.1666667 0.3333333), wk = 0.0092593
379
+ k( 46) = ( 0.1666667 0.1666667 -0.5000000), wk = 0.0092593
380
+ k( 47) = ( 0.1666667 0.1666667 -0.3333333), wk = 0.0092593
381
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382
+ k( 49) = ( 0.1666667 0.3333333 0.0000000), wk = 0.0092593
383
+ k( 50) = ( 0.1666667 0.3333333 0.1666667), wk = 0.0092593
384
+ k( 51) = ( 0.1666667 0.3333333 0.3333333), wk = 0.0092593
385
+ k( 52) = ( 0.1666667 0.3333333 -0.5000000), wk = 0.0092593
386
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387
+ k( 54) = ( 0.1666667 0.3333333 -0.1666667), wk = 0.0092593
388
+ k( 55) = ( 0.1666667 -0.5000000 0.0000000), wk = 0.0092593
389
+ k( 56) = ( 0.1666667 -0.5000000 0.1666667), wk = 0.0092593
390
+ k( 57) = ( 0.1666667 -0.5000000 0.3333333), wk = 0.0092593
391
+ k( 58) = ( 0.1666667 -0.5000000 -0.5000000), wk = 0.0092593
392
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393
+ k( 60) = ( 0.1666667 -0.5000000 -0.1666667), wk = 0.0092593
394
+ k( 61) = ( 0.1666667 -0.3333333 0.0000000), wk = 0.0092593
395
+ k( 62) = ( 0.1666667 -0.3333333 0.1666667), wk = 0.0092593
396
+ k( 63) = ( 0.1666667 -0.3333333 0.3333333), wk = 0.0092593
397
+ k( 64) = ( 0.1666667 -0.3333333 -0.5000000), wk = 0.0092593
398
+ k( 65) = ( 0.1666667 -0.3333333 -0.3333333), wk = 0.0092593
399
+ k( 66) = ( 0.1666667 -0.3333333 -0.1666667), wk = 0.0092593
400
+ k( 67) = ( 0.1666667 -0.1666667 0.0000000), wk = 0.0092593
401
+ k( 68) = ( 0.1666667 -0.1666667 0.1666667), wk = 0.0092593
402
+ k( 69) = ( 0.1666667 -0.1666667 0.3333333), wk = 0.0092593
403
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404
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405
+ k( 72) = ( 0.1666667 -0.1666667 -0.1666667), wk = 0.0092593
406
+ k( 73) = ( 0.3333333 0.0000000 0.0000000), wk = 0.0092593
407
+ k( 74) = ( 0.3333333 0.0000000 0.1666667), wk = 0.0092593
408
+ k( 75) = ( 0.3333333 0.0000000 0.3333333), wk = 0.0092593
409
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410
+ k( 77) = ( 0.3333333 0.0000000 -0.3333333), wk = 0.0092593
411
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412
+ k( 79) = ( 0.3333333 0.1666667 0.0000000), wk = 0.0092593
413
+ k( 80) = ( 0.3333333 0.1666667 0.1666667), wk = 0.0092593
414
+ k( 81) = ( 0.3333333 0.1666667 0.3333333), wk = 0.0092593
415
+ k( 82) = ( 0.3333333 0.1666667 -0.5000000), wk = 0.0092593
416
+ k( 83) = ( 0.3333333 0.1666667 -0.3333333), wk = 0.0092593
417
+ k( 84) = ( 0.3333333 0.1666667 -0.1666667), wk = 0.0092593
418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
+ k( 96) = ( 0.3333333 -0.5000000 -0.1666667), wk = 0.0092593
430
+ k( 97) = ( 0.3333333 -0.3333333 0.0000000), wk = 0.0092593
431
+ k( 98) = ( 0.3333333 -0.3333333 0.1666667), wk = 0.0092593
432
+ k( 99) = ( 0.3333333 -0.3333333 0.3333333), wk = 0.0092593
433
+ k( 100) = ( 0.3333333 -0.3333333 -0.5000000), wk = 0.0092593
434
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435
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436
+ k( 103) = ( 0.3333333 -0.1666667 0.0000000), wk = 0.0092593
437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
+ k( 117) = ( -0.5000000 0.1666667 0.3333333), wk = 0.0092593
451
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452
+ k( 119) = ( -0.5000000 0.1666667 -0.3333333), wk = 0.0092593
453
+ k( 120) = ( -0.5000000 0.1666667 -0.1666667), wk = 0.0092593
454
+ k( 121) = ( -0.5000000 0.3333333 0.0000000), wk = 0.0092593
455
+ k( 122) = ( -0.5000000 0.3333333 0.1666667), wk = 0.0092593
456
+ k( 123) = ( -0.5000000 0.3333333 0.3333333), wk = 0.0092593
457
+ k( 124) = ( -0.5000000 0.3333333 -0.5000000), wk = 0.0092593
458
+ k( 125) = ( -0.5000000 0.3333333 -0.3333333), wk = 0.0092593
459
+ k( 126) = ( -0.5000000 0.3333333 -0.1666667), wk = 0.0092593
460
+ k( 127) = ( -0.5000000 -0.5000000 0.0000000), wk = 0.0092593
461
+ k( 128) = ( -0.5000000 -0.5000000 0.1666667), wk = 0.0092593
462
+ k( 129) = ( -0.5000000 -0.5000000 0.3333333), wk = 0.0092593
463
+ k( 130) = ( -0.5000000 -0.5000000 -0.5000000), wk = 0.0092593
464
+ k( 131) = ( -0.5000000 -0.5000000 -0.3333333), wk = 0.0092593
465
+ k( 132) = ( -0.5000000 -0.5000000 -0.1666667), wk = 0.0092593
466
+ k( 133) = ( -0.5000000 -0.3333333 0.0000000), wk = 0.0092593
467
+ k( 134) = ( -0.5000000 -0.3333333 0.1666667), wk = 0.0092593
468
+ k( 135) = ( -0.5000000 -0.3333333 0.3333333), wk = 0.0092593
469
+ k( 136) = ( -0.5000000 -0.3333333 -0.5000000), wk = 0.0092593
470
+ k( 137) = ( -0.5000000 -0.3333333 -0.3333333), wk = 0.0092593
471
+ k( 138) = ( -0.5000000 -0.3333333 -0.1666667), wk = 0.0092593
472
+ k( 139) = ( -0.5000000 -0.1666667 0.0000000), wk = 0.0092593
473
+ k( 140) = ( -0.5000000 -0.1666667 0.1666667), wk = 0.0092593
474
+ k( 141) = ( -0.5000000 -0.1666667 0.3333333), wk = 0.0092593
475
+ k( 142) = ( -0.5000000 -0.1666667 -0.5000000), wk = 0.0092593
476
+ k( 143) = ( -0.5000000 -0.1666667 -0.3333333), wk = 0.0092593
477
+ k( 144) = ( -0.5000000 -0.1666667 -0.1666667), wk = 0.0092593
478
+ k( 145) = ( -0.3333333 0.0000000 0.0000000), wk = 0.0092593
479
+ k( 146) = ( -0.3333333 0.0000000 0.1666667), wk = 0.0092593
480
+ k( 147) = ( -0.3333333 0.0000000 0.3333333), wk = 0.0092593
481
+ k( 148) = ( -0.3333333 0.0000000 -0.5000000), wk = 0.0092593
482
+ k( 149) = ( -0.3333333 0.0000000 -0.3333333), wk = 0.0092593
483
+ k( 150) = ( -0.3333333 0.0000000 -0.1666667), wk = 0.0092593
484
+ k( 151) = ( -0.3333333 0.1666667 0.0000000), wk = 0.0092593
485
+ k( 152) = ( -0.3333333 0.1666667 0.1666667), wk = 0.0092593
486
+ k( 153) = ( -0.3333333 0.1666667 0.3333333), wk = 0.0092593
487
+ k( 154) = ( -0.3333333 0.1666667 -0.5000000), wk = 0.0092593
488
+ k( 155) = ( -0.3333333 0.1666667 -0.3333333), wk = 0.0092593
489
+ k( 156) = ( -0.3333333 0.1666667 -0.1666667), wk = 0.0092593
490
+ k( 157) = ( -0.3333333 0.3333333 0.0000000), wk = 0.0092593
491
+ k( 158) = ( -0.3333333 0.3333333 0.1666667), wk = 0.0092593
492
+ k( 159) = ( -0.3333333 0.3333333 0.3333333), wk = 0.0092593
493
+ k( 160) = ( -0.3333333 0.3333333 -0.5000000), wk = 0.0092593
494
+ k( 161) = ( -0.3333333 0.3333333 -0.3333333), wk = 0.0092593
495
+ k( 162) = ( -0.3333333 0.3333333 -0.1666667), wk = 0.0092593
496
+ k( 163) = ( -0.3333333 -0.5000000 0.0000000), wk = 0.0092593
497
+ k( 164) = ( -0.3333333 -0.5000000 0.1666667), wk = 0.0092593
498
+ k( 165) = ( -0.3333333 -0.5000000 0.3333333), wk = 0.0092593
499
+ k( 166) = ( -0.3333333 -0.5000000 -0.5000000), wk = 0.0092593
500
+ k( 167) = ( -0.3333333 -0.5000000 -0.3333333), wk = 0.0092593
501
+ k( 168) = ( -0.3333333 -0.5000000 -0.1666667), wk = 0.0092593
502
+ k( 169) = ( -0.3333333 -0.3333333 0.0000000), wk = 0.0092593
503
+ k( 170) = ( -0.3333333 -0.3333333 0.1666667), wk = 0.0092593
504
+ k( 171) = ( -0.3333333 -0.3333333 0.3333333), wk = 0.0092593
505
+ k( 172) = ( -0.3333333 -0.3333333 -0.5000000), wk = 0.0092593
506
+ k( 173) = ( -0.3333333 -0.3333333 -0.3333333), wk = 0.0092593
507
+ k( 174) = ( -0.3333333 -0.3333333 -0.1666667), wk = 0.0092593
508
+ k( 175) = ( -0.3333333 -0.1666667 0.0000000), wk = 0.0092593
509
+ k( 176) = ( -0.3333333 -0.1666667 0.1666667), wk = 0.0092593
510
+ k( 177) = ( -0.3333333 -0.1666667 0.3333333), wk = 0.0092593
511
+ k( 178) = ( -0.3333333 -0.1666667 -0.5000000), wk = 0.0092593
512
+ k( 179) = ( -0.3333333 -0.1666667 -0.3333333), wk = 0.0092593
513
+ k( 180) = ( -0.3333333 -0.1666667 -0.1666667), wk = 0.0092593
514
+ k( 181) = ( -0.1666667 0.0000000 0.0000000), wk = 0.0092593
515
+ k( 182) = ( -0.1666667 0.0000000 0.1666667), wk = 0.0092593
516
+ k( 183) = ( -0.1666667 0.0000000 0.3333333), wk = 0.0092593
517
+ k( 184) = ( -0.1666667 0.0000000 -0.5000000), wk = 0.0092593
518
+ k( 185) = ( -0.1666667 0.0000000 -0.3333333), wk = 0.0092593
519
+ k( 186) = ( -0.1666667 0.0000000 -0.1666667), wk = 0.0092593
520
+ k( 187) = ( -0.1666667 0.1666667 0.0000000), wk = 0.0092593
521
+ k( 188) = ( -0.1666667 0.1666667 0.1666667), wk = 0.0092593
522
+ k( 189) = ( -0.1666667 0.1666667 0.3333333), wk = 0.0092593
523
+ k( 190) = ( -0.1666667 0.1666667 -0.5000000), wk = 0.0092593
524
+ k( 191) = ( -0.1666667 0.1666667 -0.3333333), wk = 0.0092593
525
+ k( 192) = ( -0.1666667 0.1666667 -0.1666667), wk = 0.0092593
526
+ k( 193) = ( -0.1666667 0.3333333 0.0000000), wk = 0.0092593
527
+ k( 194) = ( -0.1666667 0.3333333 0.1666667), wk = 0.0092593
528
+ k( 195) = ( -0.1666667 0.3333333 0.3333333), wk = 0.0092593
529
+ k( 196) = ( -0.1666667 0.3333333 -0.5000000), wk = 0.0092593
530
+ k( 197) = ( -0.1666667 0.3333333 -0.3333333), wk = 0.0092593
531
+ k( 198) = ( -0.1666667 0.3333333 -0.1666667), wk = 0.0092593
532
+ k( 199) = ( -0.1666667 -0.5000000 0.0000000), wk = 0.0092593
533
+ k( 200) = ( -0.1666667 -0.5000000 0.1666667), wk = 0.0092593
534
+ k( 201) = ( -0.1666667 -0.5000000 0.3333333), wk = 0.0092593
535
+ k( 202) = ( -0.1666667 -0.5000000 -0.5000000), wk = 0.0092593
536
+ k( 203) = ( -0.1666667 -0.5000000 -0.3333333), wk = 0.0092593
537
+ k( 204) = ( -0.1666667 -0.5000000 -0.1666667), wk = 0.0092593
538
+ k( 205) = ( -0.1666667 -0.3333333 0.0000000), wk = 0.0092593
539
+ k( 206) = ( -0.1666667 -0.3333333 0.1666667), wk = 0.0092593
540
+ k( 207) = ( -0.1666667 -0.3333333 0.3333333), wk = 0.0092593
541
+ k( 208) = ( -0.1666667 -0.3333333 -0.5000000), wk = 0.0092593
542
+ k( 209) = ( -0.1666667 -0.3333333 -0.3333333), wk = 0.0092593
543
+ k( 210) = ( -0.1666667 -0.3333333 -0.1666667), wk = 0.0092593
544
+ k( 211) = ( -0.1666667 -0.1666667 0.0000000), wk = 0.0092593
545
+ k( 212) = ( -0.1666667 -0.1666667 0.1666667), wk = 0.0092593
546
+ k( 213) = ( -0.1666667 -0.1666667 0.3333333), wk = 0.0092593
547
+ k( 214) = ( -0.1666667 -0.1666667 -0.5000000), wk = 0.0092593
548
+ k( 215) = ( -0.1666667 -0.1666667 -0.3333333), wk = 0.0092593
549
+ k( 216) = ( -0.1666667 -0.1666667 -0.1666667), wk = 0.0092593
550
+
551
+ Dense grid: 4909 G-vectors FFT dimensions: ( 24, 24, 24)
552
+
553
+ Dynamical RAM for wfc: 0.00 MB
554
+
555
+ Dynamical RAM for wfc (w. buffer): 0.99 MB
556
+
557
+ Dynamical RAM for str. fact: 0.01 MB
558
+
559
+ Dynamical RAM for local pot: 0.00 MB
560
+
561
+ Dynamical RAM for nlocal pot: 0.02 MB
562
+
563
+ Dynamical RAM for qrad: 0.36 MB
564
+
565
+ Dynamical RAM for rho,v,vnew: 0.07 MB
566
+
567
+ Dynamical RAM for rhoin: 0.02 MB
568
+
569
+ Dynamical RAM for rho*nmix: 0.15 MB
570
+
571
+ Dynamical RAM for G-vectors: 0.04 MB
572
+
573
+ Dynamical RAM for h,s,v(r/c): 0.00 MB
574
+
575
+ Dynamical RAM for <psi|beta>: 0.00 MB
576
+
577
+ Dynamical RAM for psi: 0.01 MB
578
+
579
+ Dynamical RAM for hpsi: 0.01 MB
580
+
581
+ Dynamical RAM for wfcinit/wfcrot: 0.02 MB
582
+
583
+ Estimated static dynamical RAM per process > 1.59 MB
584
+
585
+ Estimated max dynamical RAM per process > 1.76 MB
586
+
587
+ Estimated total dynamical RAM > 14.11 MB
588
+
589
+ Initial potential from superposition of free atoms
590
+
591
+ starting charge 7.9999, renormalised to 8.0000
592
+ Starting wfcs are 8 randomized atomic wfcs
593
+
594
+ total cpu time spent up to now is 0.2 secs
595
+
596
+ Self-consistent Calculation
597
+
598
+ iteration # 1 ecut= 60.00 Ry beta= 0.70
599
+ Davidson diagonalization with overlap
600
+
601
+ ---- Real-time Memory Report at c_bands before calling an iterative solver
602
+ 66 MiB given to the printing process from OS
603
+ 0 MiB allocation reported by mallinfo(arena+hblkhd)
604
+ 768 MiB available memory on the node where the printing process lives
605
+ ------------------
606
+ ethr = 1.00E-02, avg # of iterations = 2.0
607
+
608
+ total cpu time spent up to now is 0.4 secs
609
+
610
+ total energy = -24.06023638 Ry
611
+ estimated scf accuracy < 0.10756449 Ry
612
+
613
+ iteration # 2 ecut= 60.00 Ry beta= 0.70
614
+ Davidson diagonalization with overlap
615
+
616
+ ---- Real-time Memory Report at c_bands before calling an iterative solver
617
+ 67 MiB given to the printing process from OS
618
+ 0 MiB allocation reported by mallinfo(arena+hblkhd)
619
+ 773 MiB available memory on the node where the printing process lives
620
+ ------------------
621
+ ethr = 1.34E-03, avg # of iterations = 1.3
622
+
623
+ total cpu time spent up to now is 0.6 secs
624
+
625
+ total energy = -24.06652295 Ry
626
+ estimated scf accuracy < 0.00374865 Ry
627
+
628
+ iteration # 3 ecut= 60.00 Ry beta= 0.70
629
+ Davidson diagonalization with overlap
630
+
631
+ ---- Real-time Memory Report at c_bands before calling an iterative solver
632
+ 67 MiB given to the printing process from OS
633
+ 0 MiB allocation reported by mallinfo(arena+hblkhd)
634
+ 773 MiB available memory on the node where the printing process lives
635
+ ------------------
636
+ ethr = 4.69E-05, avg # of iterations = 2.8
637
+
638
+ total cpu time spent up to now is 0.9 secs
639
+
640
+ total energy = -24.06800308 Ry
641
+ estimated scf accuracy < 0.00008274 Ry
642
+
643
+ iteration # 4 ecut= 60.00 Ry beta= 0.70
644
+ Davidson diagonalization with overlap
645
+
646
+ ---- Real-time Memory Report at c_bands before calling an iterative solver
647
+ 67 MiB given to the printing process from OS
648
+ 0 MiB allocation reported by mallinfo(arena+hblkhd)
649
+ 773 MiB available memory on the node where the printing process lives
650
+ ------------------
651
+ ethr = 1.03E-06, avg # of iterations = 4.7
652
+
653
+ total cpu time spent up to now is 1.3 secs
654
+
655
+ total energy = -24.06809223 Ry
656
+ estimated scf accuracy < 0.00001198 Ry
657
+
658
+ iteration # 5 ecut= 60.00 Ry beta= 0.70
659
+ Davidson diagonalization with overlap
660
+
661
+ ---- Real-time Memory Report at c_bands before calling an iterative solver
662
+ 67 MiB given to the printing process from OS
663
+ 0 MiB allocation reported by mallinfo(arena+hblkhd)
664
+ 770 MiB available memory on the node where the printing process lives
665
+ ------------------
666
+ ethr = 1.50E-07, avg # of iterations = 2.6
667
+
668
+ total cpu time spent up to now is 1.6 secs
669
+
670
+ total energy = -24.06809566 Ry
671
+ estimated scf accuracy < 0.00000084 Ry
672
+
673
+ iteration # 6 ecut= 60.00 Ry beta= 0.70
674
+ Davidson diagonalization with overlap
675
+
676
+ ---- Real-time Memory Report at c_bands before calling an iterative solver
677
+ 67 MiB given to the printing process from OS
678
+ 0 MiB allocation reported by mallinfo(arena+hblkhd)
679
+ 774 MiB available memory on the node where the printing process lives
680
+ ------------------
681
+ ethr = 1.05E-08, avg # of iterations = 3.1
682
+
683
+ total cpu time spent up to now is 1.8 secs
684
+
685
+ total energy = -24.06809595 Ry
686
+ estimated scf accuracy < 0.00000001 Ry
687
+
688
+ iteration # 7 ecut= 60.00 Ry beta= 0.70
689
+ Davidson diagonalization with overlap
690
+
691
+ ---- Real-time Memory Report at c_bands before calling an iterative solver
692
+ 67 MiB given to the printing process from OS
693
+ 0 MiB allocation reported by mallinfo(arena+hblkhd)
694
+ 778 MiB available memory on the node where the printing process lives
695
+ ------------------
696
+ ethr = 1.75E-10, avg # of iterations = 4.9
697
+
698
+ total cpu time spent up to now is 2.2 secs
699
+
700
+ total energy = -24.06809597 Ry
701
+ estimated scf accuracy < 2.8E-10 Ry
702
+
703
+ iteration # 8 ecut= 60.00 Ry beta= 0.70
704
+ Davidson diagonalization with overlap
705
+
706
+ ---- Real-time Memory Report at c_bands before calling an iterative solver
707
+ 67 MiB given to the printing process from OS
708
+ 0 MiB allocation reported by mallinfo(arena+hblkhd)
709
+ 795 MiB available memory on the node where the printing process lives
710
+ ------------------
711
+ ethr = 3.52E-12, avg # of iterations = 4.7
712
+
713
+ total cpu time spent up to now is 2.6 secs
714
+
715
+ total energy = -24.06809597 Ry
716
+ estimated scf accuracy < 5.0E-11 Ry
717
+
718
+ iteration # 9 ecut= 60.00 Ry beta= 0.70
719
+ Davidson diagonalization with overlap
720
+
721
+ ---- Real-time Memory Report at c_bands before calling an iterative solver
722
+ 67 MiB given to the printing process from OS
723
+ 0 MiB allocation reported by mallinfo(arena+hblkhd)
724
+ 788 MiB available memory on the node where the printing process lives
725
+ ------------------
726
+ ethr = 6.28E-13, avg # of iterations = 2.0
727
+
728
+ total cpu time spent up to now is 2.8 secs
729
+
730
+ total energy = -24.06809597 Ry
731
+ estimated scf accuracy < 4.4E-12 Ry
732
+
733
+ iteration # 10 ecut= 60.00 Ry beta= 0.70
734
+ Davidson diagonalization with overlap
735
+
736
+ ---- Real-time Memory Report at c_bands before calling an iterative solver
737
+ 67 MiB given to the printing process from OS
738
+ 0 MiB allocation reported by mallinfo(arena+hblkhd)
739
+ 789 MiB available memory on the node where the printing process lives
740
+ ------------------
741
+ ethr = 1.00E-13, avg # of iterations = 2.3
742
+
743
+ total cpu time spent up to now is 3.1 secs
744
+
745
+ total energy = -24.06809597 Ry
746
+ estimated scf accuracy < 3.6E-13 Ry
747
+
748
+ iteration # 11 ecut= 60.00 Ry beta= 0.70
749
+ Davidson diagonalization with overlap
750
+
751
+ ---- Real-time Memory Report at c_bands before calling an iterative solver
752
+ 67 MiB given to the printing process from OS
753
+ 0 MiB allocation reported by mallinfo(arena+hblkhd)
754
+ 789 MiB available memory on the node where the printing process lives
755
+ ------------------
756
+ ethr = 1.00E-13, avg # of iterations = 1.0
757
+
758
+ total cpu time spent up to now is 3.3 secs
759
+
760
+ End of self-consistent calculation
761
+
762
+ k = 0.0000 0.0000 0.0000 ( 609 PWs) bands (ev):
763
+
764
+ -8.1313 13.3565 13.3647 13.3728
765
+
766
+ occupation numbers
767
+ 1.0000 1.0000 1.0000 1.0000
768
+
769
+ k = 0.1179 0.1179-0.1179 ( 585 PWs) bands (ev):
770
+
771
+ -7.3648 9.6068 12.3467 12.3532
772
+
773
+ occupation numbers
774
+ 1.0000 1.0000 1.0000 1.0000
775
+
776
+ k = 0.2357 0.2357-0.2357 ( 601 PWs) bands (ev):
777
+
778
+ -5.1170 3.9786 11.0410 11.0471
779
+
780
+ occupation numbers
781
+ 1.0000 1.0000 1.0000 1.0000
782
+
783
+ k =-0.3536-0.3536 0.3536 ( 610 PWs) bands (ev):
784
+
785
+ -2.3563 -0.0312 10.5558 10.5619
786
+
787
+ occupation numbers
788
+ 1.0000 1.0000 1.0000 1.0000
789
+
790
+ k =-0.2357-0.2357 0.2357 ( 601 PWs) bands (ev):
791
+
792
+ -5.1170 3.9786 11.0410 11.0471
793
+
794
+ occupation numbers
795
+ 1.0000 1.0000 1.0000 1.0000
796
+
797
+ k =-0.1179-0.1179 0.1179 ( 585 PWs) bands (ev):
798
+
799
+ -7.3648 9.6068 12.3467 12.3532
800
+
801
+ occupation numbers
802
+ 1.0000 1.0000 1.0000 1.0000
803
+
804
+ k = 0.1179-0.1179 0.1179 ( 585 PWs) bands (ev):
805
+
806
+ -7.3648 9.6105 12.3420 12.3533
807
+
808
+ occupation numbers
809
+ 1.0000 1.0000 1.0000 1.0000
810
+
811
+ k = 0.2357 0.0000 0.0000 ( 584 PWs) bands (ev):
812
+
813
+ -7.1100 10.7932 10.7993 11.1284
814
+
815
+ occupation numbers
816
+ 1.0000 1.0000 1.0000 1.0000
817
+
818
+ k = 0.3536 0.1179-0.1179 ( 606 PWs) bands (ev):
819
+
820
+ -5.3541 6.1284 9.5262 9.9345
821
+
822
+ occupation numbers
823
+ 1.0000 1.0000 1.0000 1.0000
824
+
825
+ k =-0.2357-0.4714 0.4714 ( 606 PWs) bands (ev):
826
+
827
+ -2.3791 1.3327 8.3686 9.3914
828
+
829
+ occupation numbers
830
+ 1.0000 1.0000 1.0000 1.0000
831
+
832
+ k =-0.1179-0.3536 0.3536 ( 596 PWs) bands (ev):
833
+
834
+ -3.4456 2.8115 7.6831 10.4031
835
+
836
+ occupation numbers
837
+ 1.0000 1.0000 1.0000 1.0000
838
+
839
+ k = 0.0000-0.2357 0.2357 ( 596 PWs) bands (ev):
840
+
841
+ -6.1030 7.6576 8.5531 11.9953
842
+
843
+ occupation numbers
844
+ 1.0000 1.0000 1.0000 1.0000
845
+
846
+ k = 0.2357-0.2357 0.2357 ( 601 PWs) bands (ev):
847
+
848
+ -5.1170 3.9808 11.0376 11.0482
849
+
850
+ occupation numbers
851
+ 1.0000 1.0000 1.0000 1.0000
852
+
853
+ k = 0.3536-0.1179 0.1179 ( 606 PWs) bands (ev):
854
+
855
+ -5.3541 6.1316 9.5270 9.9296
856
+
857
+ occupation numbers
858
+ 1.0000 1.0000 1.0000 1.0000
859
+
860
+ k = 0.4714 0.0000 0.0000 ( 612 PWs) bands (ev):
861
+
862
+ -4.1120 6.1718 8.0398 8.0439
863
+
864
+ occupation numbers
865
+ 1.0000 1.0000 1.0000 1.0000
866
+
867
+ k =-0.1179-0.5893 0.5893 ( 603 PWs) bands (ev):
868
+
869
+ -1.5294 3.1257 5.7034 7.7154
870
+
871
+ occupation numbers
872
+ 1.0000 1.0000 1.0000 1.0000
873
+
874
+ k =-0.0000-0.4714 0.4714 ( 606 PWs) bands (ev):
875
+
876
+ -0.7096 2.4078 3.9783 8.6711
877
+
878
+ occupation numbers
879
+ 1.0000 1.0000 1.0000 1.0000
880
+
881
+ k = 0.1179-0.3536 0.3536 ( 596 PWs) bands (ev):
882
+
883
+ -3.4458 2.8136 7.6804 10.4041
884
+
885
+ occupation numbers
886
+ 1.0000 1.0000 1.0000 1.0000
887
+
888
+ k =-0.3536 0.3536-0.3536 ( 610 PWs) bands (ev):
889
+
890
+ -2.3576 -0.0282 10.5527 10.5632
891
+
892
+ occupation numbers
893
+ 1.0000 1.0000 1.0000 1.0000
894
+
895
+ k =-0.2357 0.4714-0.4714 ( 606 PWs) bands (ev):
896
+
897
+ -2.3798 1.3354 8.3653 9.3925
898
+
899
+ occupation numbers
900
+ 1.0000 1.0000 1.0000 1.0000
901
+
902
+ k =-0.1179 0.5893-0.5893 ( 603 PWs) bands (ev):
903
+
904
+ -1.5297 3.1285 5.7005 7.7156
905
+
906
+ occupation numbers
907
+ 1.0000 1.0000 1.0000 1.0000
908
+
909
+ k =-0.7071 0.0000 0.0000 ( 620 PWs) bands (ev):
910
+
911
+ 0.5793 0.5823 7.0479 7.0515
912
+
913
+ occupation numbers
914
+ 1.0000 1.0000 1.0000 1.0000
915
+
916
+ k =-0.5893 0.1179-0.1179 ( 603 PWs) bands (ev):
917
+
918
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919
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1912
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1922
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1924
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1928
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1929
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1931
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1933
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1938
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1943
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1947
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1948
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1949
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1950
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1952
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1954
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1956
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1957
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1959
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1961
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1963
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1964
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1966
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1968
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1969
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1970
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1971
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1973
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1975
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1977
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1978
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1980
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1982
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1984
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1985
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1987
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1988
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1989
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1990
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1991
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1992
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1993
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1994
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1995
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1996
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1997
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1998
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1999
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2000
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2001
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2002
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2003
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2004
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2005
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2006
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2007
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2008
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2010
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2011
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2012
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2013
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2015
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2017
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2018
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2019
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2020
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2022
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+
2024
+ -7.3648 9.6105 12.3420 12.3533
2025
+
2026
+ occupation numbers
2027
+ 1.0000 1.0000 1.0000 1.0000
2028
+
2029
+ k = 0.2357 0.0000-0.2357 ( 596 PWs) bands (ev):
2030
+
2031
+ -6.1030 7.6576 8.5531 11.9953
2032
+
2033
+ occupation numbers
2034
+ 1.0000 1.0000 1.0000 1.0000
2035
+
2036
+ k = 0.3536 0.1179-0.3536 ( 596 PWs) bands (ev):
2037
+
2038
+ -3.4456 2.8115 7.6831 10.4031
2039
+
2040
+ occupation numbers
2041
+ 1.0000 1.0000 1.0000 1.0000
2042
+
2043
+ k =-0.2357-0.4714 0.2357 ( 606 PWs) bands (ev):
2044
+
2045
+ -2.3791 1.3327 8.3686 9.3914
2046
+
2047
+ occupation numbers
2048
+ 1.0000 1.0000 1.0000 1.0000
2049
+
2050
+ k =-0.1179-0.3536 0.1179 ( 606 PWs) bands (ev):
2051
+
2052
+ -5.3541 6.1284 9.5262 9.9345
2053
+
2054
+ occupation numbers
2055
+ 1.0000 1.0000 1.0000 1.0000
2056
+
2057
+ k = 0.0000-0.2357 0.0000 ( 584 PWs) bands (ev):
2058
+
2059
+ -7.1100 10.7932 10.7993 11.1284
2060
+
2061
+ occupation numbers
2062
+ 1.0000 1.0000 1.0000 1.0000
2063
+
2064
+ k = 0.2357-0.2357 0.0000 ( 596 PWs) bands (ev):
2065
+
2066
+ -6.1030 7.6608 8.5528 11.9909
2067
+
2068
+ occupation numbers
2069
+ 1.0000 1.0000 1.0000 1.0000
2070
+
2071
+ k = 0.3536-0.1179-0.1179 ( 606 PWs) bands (ev):
2072
+
2073
+ -5.3541 6.1301 9.5210 9.9381
2074
+
2075
+ occupation numbers
2076
+ 1.0000 1.0000 1.0000 1.0000
2077
+
2078
+ k = 0.4714 0.0000-0.2357 ( 598 PWs) bands (ev):
2079
+
2080
+ -3.1760 4.6546 6.0439 8.5581
2081
+
2082
+ occupation numbers
2083
+ 1.0000 1.0000 1.0000 1.0000
2084
+
2085
+ k =-0.1179-0.5893 0.3536 ( 608 PWs) bands (ev):
2086
+
2087
+ -0.5787 1.6291 5.5302 7.4342
2088
+
2089
+ occupation numbers
2090
+ 1.0000 1.0000 1.0000 1.0000
2091
+
2092
+ k =-0.0000-0.4714 0.2357 ( 598 PWs) bands (ev):
2093
+
2094
+ -3.1760 4.6546 6.0439 8.5581
2095
+
2096
+ occupation numbers
2097
+ 1.0000 1.0000 1.0000 1.0000
2098
+
2099
+ k = 0.1179-0.3536 0.1179 ( 606 PWs) bands (ev):
2100
+
2101
+ -5.3541 6.1301 9.5210 9.9381
2102
+
2103
+ occupation numbers
2104
+ 1.0000 1.0000 1.0000 1.0000
2105
+
2106
+ k = 0.3536-0.3536 0.1179 ( 596 PWs) bands (ev):
2107
+
2108
+ -3.4458 2.8139 7.6831 10.4003
2109
+
2110
+ occupation numbers
2111
+ 1.0000 1.0000 1.0000 1.0000
2112
+
2113
+ k = 0.4714-0.2357 0.0000 ( 598 PWs) bands (ev):
2114
+
2115
+ -3.1761 4.6571 6.0438 8.5552
2116
+
2117
+ occupation numbers
2118
+ 1.0000 1.0000 1.0000 1.0000
2119
+
2120
+ k = 0.5893-0.1179-0.1179 ( 603 PWs) bands (ev):
2121
+
2122
+ -1.5296 3.1260 5.7071 7.7113
2123
+
2124
+ occupation numbers
2125
+ 1.0000 1.0000 1.0000 1.0000
2126
+
2127
+ k =-0.0000-0.7071 0.4714 ( 600 PWs) bands (ev):
2128
+
2129
+ 1.1223 1.1262 5.5439 5.5481
2130
+
2131
+ occupation numbers
2132
+ 1.0000 1.0000 1.0000 1.0000
2133
+
2134
+ k = 0.1179-0.5893 0.3536 ( 608 PWs) bands (ev):
2135
+
2136
+ -0.5802 1.6326 5.5312 7.4308
2137
+
2138
+ occupation numbers
2139
+ 1.0000 1.0000 1.0000 1.0000
2140
+
2141
+ k = 0.2357-0.4714 0.2357 ( 606 PWs) bands (ev):
2142
+
2143
+ -2.3797 1.3349 8.3720 9.3860
2144
+
2145
+ occupation numbers
2146
+ 1.0000 1.0000 1.0000 1.0000
2147
+
2148
+ k =-0.2357 0.2357-0.4714 ( 606 PWs) bands (ev):
2149
+
2150
+ -2.3798 1.3352 8.3686 9.3893
2151
+
2152
+ occupation numbers
2153
+ 1.0000 1.0000 1.0000 1.0000
2154
+
2155
+ k =-0.1179 0.3536-0.5893 ( 608 PWs) bands (ev):
2156
+
2157
+ -0.5801 1.6328 5.5281 7.4339
2158
+
2159
+ occupation numbers
2160
+ 1.0000 1.0000 1.0000 1.0000
2161
+
2162
+ k =-0.0000 0.4714-0.7071 ( 600 PWs) bands (ev):
2163
+
2164
+ 1.1225 1.1260 5.5440 5.5479
2165
+
2166
+ occupation numbers
2167
+ 1.0000 1.0000 1.0000 1.0000
2168
+
2169
+ k =-0.5893-0.1179-0.1179 ( 603 PWs) bands (ev):
2170
+
2171
+ -1.5299 3.1287 5.7043 7.7116
2172
+
2173
+ occupation numbers
2174
+ 1.0000 1.0000 1.0000 1.0000
2175
+
2176
+ k =-0.4714 0.0000-0.2357 ( 598 PWs) bands (ev):
2177
+
2178
+ -3.1762 4.6597 6.0437 8.5522
2179
+
2180
+ occupation numbers
2181
+ 1.0000 1.0000 1.0000 1.0000
2182
+
2183
+ k =-0.3536 0.1179-0.3536 ( 596 PWs) bands (ev):
2184
+
2185
+ -3.4459 2.8141 7.6858 10.3964
2186
+
2187
+ occupation numbers
2188
+ 1.0000 1.0000 1.0000 1.0000
2189
+
2190
+ k =-0.1179 0.1179-0.3536 ( 606 PWs) bands (ev):
2191
+
2192
+ -5.3541 6.1309 9.5240 9.9339
2193
+
2194
+ occupation numbers
2195
+ 1.0000 1.0000 1.0000 1.0000
2196
+
2197
+ k =-0.0000 0.2357-0.4714 ( 598 PWs) bands (ev):
2198
+
2199
+ -3.1760 4.6546 6.0439 8.5581
2200
+
2201
+ occupation numbers
2202
+ 1.0000 1.0000 1.0000 1.0000
2203
+
2204
+ k = 0.1179 0.3536-0.5893 ( 608 PWs) bands (ev):
2205
+
2206
+ -0.5788 1.6290 5.5333 7.4311
2207
+
2208
+ occupation numbers
2209
+ 1.0000 1.0000 1.0000 1.0000
2210
+
2211
+ k =-0.4714-0.2357 0.0000 ( 598 PWs) bands (ev):
2212
+
2213
+ -3.1761 4.6571 6.0438 8.5552
2214
+
2215
+ occupation numbers
2216
+ 1.0000 1.0000 1.0000 1.0000
2217
+
2218
+ k =-0.3536-0.1179-0.1179 ( 606 PWs) bands (ev):
2219
+
2220
+ -5.3541 6.1333 9.5218 9.9332
2221
+
2222
+ occupation numbers
2223
+ 1.0000 1.0000 1.0000 1.0000
2224
+
2225
+ k =-0.2357 0.0000-0.2357 ( 596 PWs) bands (ev):
2226
+
2227
+ -6.1030 7.6641 8.5524 11.9865
2228
+
2229
+ occupation numbers
2230
+ 1.0000 1.0000 1.0000 1.0000
2231
+
2232
+ k = 0.0000 0.0000-0.2357 ( 584 PWs) bands (ev):
2233
+
2234
+ -7.1100 10.7961 10.7963 11.1285
2235
+
2236
+ occupation numbers
2237
+ 1.0000 1.0000 1.0000 1.0000
2238
+
2239
+ k = 0.1179 0.1179-0.3536 ( 606 PWs) bands (ev):
2240
+
2241
+ -5.3541 6.1277 9.5232 9.9387
2242
+
2243
+ occupation numbers
2244
+ 1.0000 1.0000 1.0000 1.0000
2245
+
2246
+ k = 0.2357 0.2357-0.4714 ( 606 PWs) bands (ev):
2247
+
2248
+ -2.3791 1.3325 8.3720 9.3881
2249
+
2250
+ occupation numbers
2251
+ 1.0000 1.0000 1.0000 1.0000
2252
+
2253
+ k =-0.3536-0.3536 0.1179 ( 596 PWs) bands (ev):
2254
+
2255
+ -3.4457 2.8118 7.6858 10.3993
2256
+
2257
+ occupation numbers
2258
+ 1.0000 1.0000 1.0000 1.0000
2259
+
2260
+ k =-0.2357-0.2357 0.0000 ( 596 PWs) bands (ev):
2261
+
2262
+ -6.1030 7.6608 8.5528 11.9909
2263
+
2264
+ occupation numbers
2265
+ 1.0000 1.0000 1.0000 1.0000
2266
+
2267
+ k =-0.1179-0.1179-0.1179 ( 585 PWs) bands (ev):
2268
+
2269
+ -7.3648 9.6141 12.3420 12.3486
2270
+
2271
+ occupation numbers
2272
+ 1.0000 1.0000 1.0000 1.0000
2273
+
2274
+ highest occupied level (ev): 13.3728
2275
+
2276
+ ! total energy = -24.06809597 Ry
2277
+ estimated scf accuracy < 2.5E-14 Ry
2278
+
2279
+ The total energy is the sum of the following terms:
2280
+ one-electron contribution = 8.25150439 Ry
2281
+ hartree contribution = 1.93107051 Ry
2282
+ xc contribution = -8.67784739 Ry
2283
+ ewald contribution = -25.57282348 Ry
2284
+
2285
+ convergence has been achieved in 11 iterations
2286
+
2287
+ Forces acting on atoms (cartesian axes, Ry/au):
2288
+
2289
+ atom 1 type 1 force = 0.00053393 0.00053393 -0.00000065
2290
+ atom 2 type 1 force = -0.00053393 -0.00053393 0.00000065
2291
+ The non-local contrib. to forces
2292
+ atom 1 type 1 force = 0.00034714 0.00034714 -0.00000034
2293
+ atom 2 type 1 force = -0.00034713 -0.00034713 0.00000036
2294
+ The ionic contribution to forces
2295
+ atom 1 type 1 force = 0.00123788 0.00123788 -0.00000238
2296
+ atom 2 type 1 force = -0.00123788 -0.00123788 0.00000238
2297
+ The local contribution to forces
2298
+ atom 1 type 1 force = -0.00102609 -0.00102609 0.00000204
2299
+ atom 2 type 1 force = 0.00102609 0.00102609 -0.00000205
2300
+ The core correction contribution to forces
2301
+ atom 1 type 1 force = -0.00002499 -0.00002499 0.00000004
2302
+ atom 2 type 1 force = 0.00002499 0.00002499 -0.00000004
2303
+ The Hubbard contrib. to forces
2304
+ atom 1 type 1 force = 0.00000000 0.00000000 0.00000000
2305
+ atom 2 type 1 force = 0.00000000 0.00000000 0.00000000
2306
+ The SCF correction term to forces
2307
+ atom 1 type 1 force = -0.00000000 -0.00000000 0.00000001
2308
+ atom 2 type 1 force = 0.00000000 0.00000000 0.00000000
2309
+
2310
+ Total force = 0.001068 Total SCF correction = 0.000000
2311
+
2312
+ Writing all to output data dir ./tmp/scf.save/
2313
+
2314
+ init_run : 0.10s CPU 0.15s WALL ( 1 calls)
2315
+ electrons : 2.03s CPU 3.13s WALL ( 1 calls)
2316
+ forces : 0.01s CPU 0.02s WALL ( 1 calls)
2317
+
2318
+ Called by init_run:
2319
+ wfcinit : 0.09s CPU 0.14s WALL ( 1 calls)
2320
+ wfcinit:atom : 0.00s CPU 0.00s WALL ( 216 calls)
2321
+ wfcinit:wfcr : 0.08s CPU 0.13s WALL ( 216 calls)
2322
+ potinit : 0.00s CPU 0.00s WALL ( 1 calls)
2323
+ hinit0 : 0.01s CPU 0.01s WALL ( 1 calls)
2324
+
2325
+ Called by electrons:
2326
+ c_bands : 1.81s CPU 2.80s WALL ( 11 calls)
2327
+ sum_band : 0.21s CPU 0.32s WALL ( 11 calls)
2328
+ v_of_rho : 0.01s CPU 0.01s WALL ( 12 calls)
2329
+ v_h : 0.00s CPU 0.00s WALL ( 12 calls)
2330
+ v_xc : 0.01s CPU 0.01s WALL ( 13 calls)
2331
+ mix_rho : 0.00s CPU 0.00s WALL ( 11 calls)
2332
+
2333
+ Called by c_bands:
2334
+ init_us_2 : 0.04s CPU 0.07s WALL ( 5184 calls)
2335
+ init_us_2:cp : 0.03s CPU 0.05s WALL ( 5184 calls)
2336
+ cegterg : 1.72s CPU 2.66s WALL ( 2376 calls)
2337
+
2338
+ Called by sum_band:
2339
+ sum_band:wei : 0.00s CPU 0.00s WALL ( 11 calls)
2340
+ sum_band:loo : 0.21s CPU 0.32s WALL ( 11 calls)
2341
+ sum_band:buf : 0.00s CPU 0.01s WALL ( 2376 calls)
2342
+ sum_band:ini : 0.03s CPU 0.04s WALL ( 2376 calls)
2343
+
2344
+ Called by *egterg:
2345
+ cdiaghg : 0.13s CPU 0.20s WALL ( 9159 calls)
2346
+ cegterg:over : 0.08s CPU 0.12s WALL ( 6783 calls)
2347
+ cegterg:upda : 0.02s CPU 0.03s WALL ( 6783 calls)
2348
+ cegterg:last : 0.02s CPU 0.03s WALL ( 6030 calls)
2349
+ h_psi : 1.36s CPU 2.11s WALL ( 9375 calls)
2350
+ g_psi : 0.01s CPU 0.02s WALL ( 6783 calls)
2351
+
2352
+ Called by h_psi:
2353
+ h_psi:calbec : 0.09s CPU 0.14s WALL ( 9375 calls)
2354
+ vloc_psi : 1.16s CPU 1.79s WALL ( 9375 calls)
2355
+ add_vuspsi : 0.04s CPU 0.05s WALL ( 9375 calls)
2356
+
2357
+ General routines
2358
+ calbec : 0.07s CPU 0.11s WALL ( 10239 calls)
2359
+ fft : 0.01s CPU 0.01s WALL ( 130 calls)
2360
+ ffts : 0.00s CPU 0.00s WALL ( 11 calls)
2361
+ fftw : 1.15s CPU 1.78s WALL ( 73844 calls)
2362
+
2363
+ Parallel routines
2364
+
2365
+ PWSCF : 2.17s CPU 3.37s WALL
2366
+
2367
+
2368
+ This run was terminated on: 20:38:53 19Feb2026
2369
+
2370
+ =------------------------------------------------------------------------------=
2371
+ JOB DONE.
2372
+ =------------------------------------------------------------------------------=
3_epc/displacements/group_6/tmp/VSC ADDED
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+ size 138152
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3_epc/displacements/group_6/tmp/scf.save/charge-density.dat ADDED
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3_epc/displacements/group_6/tmp/scf.xml ADDED
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Binary file (13.3 kB). View file
 
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_se3_transformer/__init__.py ADDED
@@ -0,0 +1 @@
 
 
1
+ from .representations import SphericalHarmonics
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Binary file (8.14 kB). View file
 
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_se3_transformer/license.txt ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ The code in this folder was obtained from "https://github.com/mariogeiger/se3cnn/", which has the following license:
2
+
3
+
4
+ MIT License
5
+
6
+ Copyright (c) 2019 Mario Geiger
7
+
8
+ Permission is hereby granted, free of charge, to any person obtaining a copy
9
+ of this software and associated documentation files (the "Software"), to deal
10
+ in the Software without restriction, including without limitation the rights
11
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
12
+ copies of the Software, and to permit persons to whom the Software is
13
+ furnished to do so, subject to the following conditions:
14
+
15
+ The above copyright notice and this permission notice shall be included in all
16
+ copies or substantial portions of the Software.
17
+
18
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
19
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
20
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
21
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
22
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
23
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
24
+ SOFTWARE.
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/from_se3_transformer/representations.py ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import numpy as np
3
+
4
+
5
+ def semifactorial(x):
6
+ """Compute the semifactorial function x!!.
7
+
8
+ x!! = x * (x-2) * (x-4) *...
9
+
10
+ Args:
11
+ x: positive int
12
+ Returns:
13
+ float for x!!
14
+ """
15
+ y = 1.
16
+ for n in range(x, 1, -2):
17
+ y *= n
18
+ return y
19
+
20
+
21
+ def pochhammer(x, k):
22
+ """Compute the pochhammer symbol (x)_k.
23
+
24
+ (x)_k = x * (x+1) * (x+2) *...* (x+k-1)
25
+
26
+ Args:
27
+ x: positive int
28
+ Returns:
29
+ float for (x)_k
30
+ """
31
+ xf = float(x)
32
+ for n in range(x+1, x+k):
33
+ xf *= n
34
+ return xf
35
+
36
+ def lpmv(l, m, x):
37
+ """Associated Legendre function including Condon-Shortley phase.
38
+
39
+ Args:
40
+ m: int order
41
+ l: int degree
42
+ x: float argument tensor
43
+ Returns:
44
+ tensor of x-shape
45
+ """
46
+ m_abs = abs(m)
47
+ if m_abs > l:
48
+ return torch.zeros_like(x)
49
+
50
+ # Compute P_m^m
51
+ yold = ((-1)**m_abs * semifactorial(2*m_abs-1)) * torch.pow(1-x*x, m_abs/2)
52
+
53
+ # Compute P_{m+1}^m
54
+ if m_abs != l:
55
+ y = x * (2*m_abs+1) * yold
56
+ else:
57
+ y = yold
58
+
59
+ # Compute P_{l}^m from recursion in P_{l-1}^m and P_{l-2}^m
60
+ for i in range(m_abs+2, l+1):
61
+ tmp = y
62
+ # Inplace speedup
63
+ y = ((2*i-1) / (i-m_abs)) * x * y
64
+ y -= ((i+m_abs-1)/(i-m_abs)) * yold
65
+ yold = tmp
66
+
67
+ if m < 0:
68
+ y *= ((-1)**m / pochhammer(l+m+1, -2*m))
69
+
70
+ return y
71
+
72
+ def tesseral_harmonics(l, m, theta=0., phi=0.):
73
+ """Tesseral spherical harmonic with Condon-Shortley phase.
74
+
75
+ The Tesseral spherical harmonics are also known as the real spherical
76
+ harmonics.
77
+
78
+ Args:
79
+ l: int for degree
80
+ m: int for order, where -l <= m < l
81
+ theta: collatitude or polar angle
82
+ phi: longitude or azimuth
83
+ Returns:
84
+ tensor of shape theta
85
+ """
86
+ assert abs(m) <= l, "absolute value of order m must be <= degree l"
87
+
88
+ N = np.sqrt((2*l+1) / (4*np.pi))
89
+ leg = lpmv(l, abs(m), torch.cos(theta))
90
+ if m == 0:
91
+ return N*leg
92
+ elif m > 0:
93
+ Y = torch.cos(m*phi) * leg
94
+ else:
95
+ Y = torch.sin(abs(m)*phi) * leg
96
+ N *= np.sqrt(2. / pochhammer(l-abs(m)+1, 2*abs(m)))
97
+ Y *= N
98
+ return Y
99
+
100
+ class SphericalHarmonics(object):
101
+ def __init__(self):
102
+ self.leg = {}
103
+
104
+ def clear(self):
105
+ self.leg = {}
106
+
107
+ def negative_lpmv(self, l, m, y):
108
+ """Compute negative order coefficients"""
109
+ if m < 0:
110
+ y *= ((-1)**m / pochhammer(l+m+1, -2*m))
111
+ return y
112
+
113
+ def lpmv(self, l, m, x):
114
+ """Associated Legendre function including Condon-Shortley phase.
115
+
116
+ Args:
117
+ m: int order
118
+ l: int degree
119
+ x: float argument tensor
120
+ Returns:
121
+ tensor of x-shape
122
+ """
123
+ # Check memoized versions
124
+ m_abs = abs(m)
125
+ if (l,m) in self.leg:
126
+ return self.leg[(l,m)]
127
+ elif m_abs > l:
128
+ return None
129
+ elif l == 0:
130
+ self.leg[(l,m)] = torch.ones_like(x)
131
+ return self.leg[(l,m)]
132
+
133
+ # Check if on boundary else recurse solution down to boundary
134
+ if m_abs == l:
135
+ # Compute P_m^m
136
+ y = (-1)**m_abs * semifactorial(2*m_abs-1)
137
+ y *= torch.pow(1-x*x, m_abs/2)
138
+ self.leg[(l,m)] = self.negative_lpmv(l, m, y)
139
+ return self.leg[(l,m)]
140
+ else:
141
+ # Recursively precompute lower degree harmonics
142
+ self.lpmv(l-1, m, x)
143
+
144
+ # Compute P_{l}^m from recursion in P_{l-1}^m and P_{l-2}^m
145
+ # Inplace speedup
146
+ y = ((2*l-1) / (l-m_abs)) * x * self.lpmv(l-1, m_abs, x)
147
+ if l - m_abs > 1:
148
+ y -= ((l+m_abs-1)/(l-m_abs)) * self.leg[(l-2, m_abs)]
149
+ #self.leg[(l, m_abs)] = y
150
+
151
+ if m < 0:
152
+ y = self.negative_lpmv(l, m, y)
153
+ self.leg[(l,m)] = y
154
+
155
+ return self.leg[(l,m)]
156
+
157
+ def get_element(self, l, m, theta, phi):
158
+ """Tesseral spherical harmonic with Condon-Shortley phase.
159
+
160
+ The Tesseral spherical harmonics are also known as the real spherical
161
+ harmonics.
162
+
163
+ Args:
164
+ l: int for degree
165
+ m: int for order, where -l <= m < l
166
+ theta: collatitude or polar angle
167
+ phi: longitude or azimuth
168
+ Returns:
169
+ tensor of shape theta
170
+ """
171
+ assert abs(m) <= l, "absolute value of order m must be <= degree l"
172
+
173
+ N = np.sqrt((2*l+1) / (4*np.pi))
174
+ leg = self.lpmv(l, abs(m), torch.cos(theta))
175
+ if m == 0:
176
+ return N*leg
177
+ elif m > 0:
178
+ Y = torch.cos(m*phi) * leg
179
+ else:
180
+ Y = torch.sin(abs(m)*phi) * leg
181
+ N *= np.sqrt(2. / pochhammer(l-abs(m)+1, 2*abs(m)))
182
+ Y *= N
183
+ return Y
184
+
185
+ def get(self, l, theta, phi, refresh=True):
186
+ """Tesseral harmonic with Condon-Shortley phase.
187
+
188
+ The Tesseral spherical harmonics are also known as the real spherical
189
+ harmonics.
190
+
191
+ Args:
192
+ l: int for degree
193
+ theta: collatitude or polar angle
194
+ phi: longitude or azimuth
195
+ Returns:
196
+ tensor of shape [*theta.shape, 2*l+1]
197
+ """
198
+ results = []
199
+ if refresh:
200
+ self.clear()
201
+ for m in range(-l, l+1):
202
+ results.append(self.get_element(l, m, theta, phi))
203
+ return torch.stack(results, -1)
204
+
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/inference/__init__.py ADDED
@@ -0,0 +1 @@
 
 
1
+ from .pred_ham import predict, predict_with_grad
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/inference/__pycache__/__init__.cpython-312.pyc ADDED
Binary file (230 Bytes). View file
 
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/inference/__pycache__/pred_ham.cpython-312.pyc ADDED
Binary file (28.8 kB). View file
 
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/inference/band_config.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "calc_job": "band",
3
+ "which_k": 0,
4
+ "fermi_level": -3.82373,
5
+ "max_iter": 300,
6
+ "num_band": 50,
7
+ "k_data": ["15 0 0 0 0.5 0.5 0 Γ M", "15 0.5 0.5 0 0.3333333333333333 0.6666666666666667 0 M K", "15 0.3333333333333333 0.6666666666666667 0 0 0 0 K Γ"]
8
+ }
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/inference/dense_calc.jl ADDED
@@ -0,0 +1,234 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ using DelimitedFiles, LinearAlgebra, JSON
2
+ using HDF5
3
+ using ArgParse
4
+ using SparseArrays
5
+ using Arpack
6
+ using JLD
7
+ # BLAS.set_num_threads(1)
8
+
9
+ const ev2Hartree = 0.036749324533634074
10
+ const Bohr2Ang = 0.529177249
11
+ const default_dtype = Complex{Float64}
12
+
13
+
14
+ function parse_commandline()
15
+ s = ArgParseSettings()
16
+ @add_arg_table! s begin
17
+ "--input_dir", "-i"
18
+ help = "path of rlat.dat, orbital_types.dat, site_positions.dat, hamiltonians_pred.h5, and overlaps.h5"
19
+ arg_type = String
20
+ default = "./"
21
+ "--output_dir", "-o"
22
+ help = "path of output openmx.Band"
23
+ arg_type = String
24
+ default = "./"
25
+ "--config"
26
+ help = "config file in the format of JSON"
27
+ arg_type = String
28
+ "--ill_project"
29
+ help = "projects out the eigenvectors of the overlap matrix that correspond to eigenvalues smaller than ill_threshold"
30
+ arg_type = Bool
31
+ default = true
32
+ "--ill_threshold"
33
+ help = "threshold for ill_project"
34
+ arg_type = Float64
35
+ default = 5e-4
36
+ end
37
+ return parse_args(s)
38
+ end
39
+
40
+
41
+ function _create_dict_h5(filename::String)
42
+ fid = h5open(filename, "r")
43
+ T = eltype(fid[keys(fid)[1]])
44
+ d_out = Dict{Array{Int64,1}, Array{T, 2}}()
45
+ for key in keys(fid)
46
+ data = read(fid[key])
47
+ nk = map(x -> parse(Int64, convert(String, x)), split(key[2 : length(key) - 1], ','))
48
+ d_out[nk] = permutedims(data)
49
+ end
50
+ close(fid)
51
+ return d_out
52
+ end
53
+
54
+
55
+ function genlist(x)
56
+ return collect(range(x[1], stop = x[2], length = Int64(x[3])))
57
+ end
58
+
59
+
60
+ function k_data2num_ks(kdata::AbstractString)
61
+ return parse(Int64,split(kdata)[1])
62
+ end
63
+
64
+
65
+ function k_data2kpath(kdata::AbstractString)
66
+ return map(x->parse(Float64,x), split(kdata)[2:7])
67
+ end
68
+
69
+
70
+ function std_out_array(a::AbstractArray)
71
+ return string(map(x->string(x," "),a)...)
72
+ end
73
+
74
+
75
+ function main()
76
+ parsed_args = parse_commandline()
77
+
78
+ println(parsed_args["config"])
79
+ config = JSON.parsefile(parsed_args["config"])
80
+ calc_job = config["calc_job"]
81
+
82
+ if isfile(joinpath(parsed_args["input_dir"],"info.json"))
83
+ spinful = JSON.parsefile(joinpath(parsed_args["input_dir"],"info.json"))["isspinful"]
84
+ else
85
+ spinful = false
86
+ end
87
+
88
+ site_positions = readdlm(joinpath(parsed_args["input_dir"], "site_positions.dat"))
89
+ nsites = size(site_positions, 2)
90
+
91
+ orbital_types_f = open(joinpath(parsed_args["input_dir"], "orbital_types.dat"), "r")
92
+ site_norbits = zeros(nsites)
93
+ orbital_types = Vector{Vector{Int64}}()
94
+ for index_site = 1:nsites
95
+ orbital_type = parse.(Int64, split(readline(orbital_types_f)))
96
+ push!(orbital_types, orbital_type)
97
+ end
98
+ site_norbits = (x->sum(x .* 2 .+ 1)).(orbital_types) * (1 + spinful)
99
+ norbits = sum(site_norbits)
100
+ site_norbits_cumsum = cumsum(site_norbits)
101
+
102
+ rlat = readdlm(joinpath(parsed_args["input_dir"], "rlat.dat"))
103
+
104
+
105
+ @info "read h5"
106
+ begin_time = time()
107
+ hamiltonians_pred = _create_dict_h5(joinpath(parsed_args["input_dir"], "hamiltonians_pred.h5"))
108
+ overlaps = _create_dict_h5(joinpath(parsed_args["input_dir"], "overlaps.h5"))
109
+ println("Time for reading h5: ", time() - begin_time, "s")
110
+
111
+ H_R = Dict{Vector{Int64}, Matrix{default_dtype}}()
112
+ S_R = Dict{Vector{Int64}, Matrix{default_dtype}}()
113
+
114
+ @info "construct Hamiltonian and overlap matrix in the real space"
115
+ begin_time = time()
116
+ for key in collect(keys(hamiltonians_pred))
117
+ hamiltonian_pred = hamiltonians_pred[key]
118
+ if (key ∈ keys(overlaps))
119
+ overlap = overlaps[key]
120
+ else
121
+ # continue
122
+ overlap = zero(hamiltonian_pred)
123
+ end
124
+ if spinful
125
+ overlap = vcat(hcat(overlap,zeros(size(overlap))),hcat(zeros(size(overlap)),overlap)) # the readout overlap matrix only contains the upper-left block # TODO maybe drop the zeros?
126
+ end
127
+ R = key[1:3]; atom_i=key[4]; atom_j=key[5]
128
+
129
+ @assert (site_norbits[atom_i], site_norbits[atom_j]) == size(hamiltonian_pred)
130
+ @assert (site_norbits[atom_i], site_norbits[atom_j]) == size(overlap)
131
+ if !(R ∈ keys(H_R))
132
+ H_R[R] = zeros(default_dtype, norbits, norbits)
133
+ S_R[R] = zeros(default_dtype, norbits, norbits)
134
+ end
135
+ for block_matrix_i in 1:site_norbits[atom_i]
136
+ for block_matrix_j in 1:site_norbits[atom_j]
137
+ index_i = site_norbits_cumsum[atom_i] - site_norbits[atom_i] + block_matrix_i
138
+ index_j = site_norbits_cumsum[atom_j] - site_norbits[atom_j] + block_matrix_j
139
+ H_R[R][index_i, index_j] = hamiltonian_pred[block_matrix_i, block_matrix_j]
140
+ S_R[R][index_i, index_j] = overlap[block_matrix_i, block_matrix_j]
141
+ end
142
+ end
143
+ end
144
+ println("Time for constructing Hamiltonian and overlap matrix in the real space: ", time() - begin_time, " s")
145
+
146
+
147
+ if calc_job == "band"
148
+ fermi_level = config["fermi_level"]
149
+ k_data = config["k_data"]
150
+
151
+ ill_project = parsed_args["ill_project"] || ("ill_project" in keys(config) && config["ill_project"])
152
+ ill_threshold = max(parsed_args["ill_threshold"], get(config, "ill_threshold", 0.))
153
+
154
+ @info "calculate bands"
155
+ num_ks = k_data2num_ks.(k_data)
156
+ kpaths = k_data2kpath.(k_data)
157
+
158
+ egvals = zeros(Float64, norbits, sum(num_ks)[1])
159
+
160
+ begin_time = time()
161
+ idx_k = 1
162
+ for i = 1:size(kpaths, 1)
163
+ kpath = kpaths[i]
164
+ pnkpts = num_ks[i]
165
+ kxs = LinRange(kpath[1], kpath[4], pnkpts)
166
+ kys = LinRange(kpath[2], kpath[5], pnkpts)
167
+ kzs = LinRange(kpath[3], kpath[6], pnkpts)
168
+ for (kx, ky, kz) in zip(kxs, kys, kzs)
169
+ idx_k
170
+ H_k = zeros(default_dtype, norbits, norbits)
171
+ S_k = zeros(default_dtype, norbits, norbits)
172
+ for R in keys(H_R)
173
+ H_k += H_R[R] * exp(im*2π*([kx, ky, kz]⋅R))
174
+ S_k += S_R[R] * exp(im*2π*([kx, ky, kz]⋅R))
175
+ end
176
+ S_k = (S_k + S_k') / 2
177
+ H_k = (H_k + H_k') / 2
178
+ if ill_project
179
+ (egval_S, egvec_S) = eigen(Hermitian(S_k))
180
+ # egvec_S: shape (num_basis, num_bands)
181
+ project_index = abs.(egval_S) .> ill_threshold
182
+ if sum(project_index) != length(project_index)
183
+ # egval_S = egval_S[project_index]
184
+ egvec_S = egvec_S[:, project_index]
185
+ @warn "ill-conditioned eigenvalues detected, projected out $(length(project_index) - sum(project_index)) eigenvalues"
186
+ H_k = egvec_S' * H_k * egvec_S
187
+ S_k = egvec_S' * S_k * egvec_S
188
+ (egval, egvec) = eigen(Hermitian(H_k), Hermitian(S_k))
189
+ egval = vcat(egval, fill(1e4, length(project_index) - sum(project_index)))
190
+ egvec = egvec_S * egvec
191
+ else
192
+ (egval, egvec) = eigen(Hermitian(H_k), Hermitian(S_k))
193
+ end
194
+ else
195
+ (egval, egvec) = eigen(Hermitian(H_k), Hermitian(S_k))
196
+ end
197
+ egvals[:, idx_k] = egval
198
+ println("Time for solving No.$idx_k eigenvalues at k = ", [kx, ky, kz], ": ", time() - begin_time, " s")
199
+ idx_k += 1
200
+ end
201
+ end
202
+
203
+ # output in openmx band format
204
+ f = open(joinpath(parsed_args["output_dir"], "openmx.Band"),"w")
205
+ println(f, norbits, " ", 0, " ", ev2Hartree * fermi_level)
206
+ openmx_rlat = reshape((rlat .* Bohr2Ang), 1, :)
207
+ println(f, std_out_array(openmx_rlat))
208
+ println(f, length(k_data))
209
+ for line in k_data
210
+ println(f,line)
211
+ end
212
+ idx_k = 1
213
+ for i = 1:size(kpaths, 1)
214
+ pnkpts = num_ks[i]
215
+ kstart = kpaths[i][1:3]
216
+ kend = kpaths[i][4:6]
217
+ k_list = zeros(Float64,pnkpts,3)
218
+ for alpha = 1:3
219
+ k_list[:,alpha] = genlist([kstart[alpha],kend[alpha],pnkpts])
220
+ end
221
+ for j = 1:pnkpts
222
+ idx_k
223
+ kvec = k_list[j,:]
224
+ println(f, norbits, " ", std_out_array(kvec))
225
+ println(f, std_out_array(ev2Hartree * egvals[:, idx_k]))
226
+ idx_k += 1
227
+ end
228
+ end
229
+ close(f)
230
+ end
231
+ end
232
+
233
+
234
+ main()
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/inference/dense_calc.py ADDED
@@ -0,0 +1,277 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json
2
+ import argparse
3
+ import h5py
4
+ import numpy as np
5
+ import os
6
+ from time import time
7
+ from scipy import linalg
8
+ import tqdm
9
+ from pathos.multiprocessing import ProcessingPool as Pool
10
+
11
+ def parse_commandline():
12
+ parser = argparse.ArgumentParser()
13
+ parser.add_argument(
14
+ "--input_dir", "-i", type=str, default="./",
15
+ help="path of rlat.dat, orbital_types.dat, site_positions.dat, hamiltonians_pred.h5, and overlaps.h5"
16
+ )
17
+ parser.add_argument(
18
+ "--output_dir", "-o", type=str, default="./",
19
+ help="path of output openmx.Band"
20
+ )
21
+ parser.add_argument(
22
+ "--config", type=str,
23
+ help="config file in the format of JSON"
24
+ )
25
+ parser.add_argument(
26
+ "--ill_project", type=bool,
27
+ help="projects out the eigenvectors of the overlap matrix that correspond to eigenvalues smaller than ill_threshold",
28
+ default=True
29
+ )
30
+ parser.add_argument(
31
+ "--ill_threshold", type=float,
32
+ help="threshold for ill_project",
33
+ default=5e-4
34
+ )
35
+ parser.add_argument(
36
+ "--multiprocessing", type=int,
37
+ help="multiprocessing for band calculation",
38
+ default=0
39
+ )
40
+ return parser.parse_args()
41
+
42
+ parsed_args = parse_commandline()
43
+
44
+ def _create_dict_h5(filename):
45
+ fid = h5py.File(filename, "r")
46
+ d_out = {}
47
+ for key in fid.keys():
48
+ data = np.array(fid[key])
49
+ nk = tuple(map(int, key[1:-1].split(',')))
50
+ # BS:
51
+ # the matrix do not need be transposed in Python,
52
+ # But the transpose should be done in Julia.
53
+ d_out[nk] = data # np.transpose(data)
54
+ fid.close()
55
+ return d_out
56
+
57
+
58
+ ev2Hartree = 0.036749324533634074
59
+ Bohr2Ang = 0.529177249
60
+
61
+
62
+ def genlist(x):
63
+ return np.linspace(x[0], x[1], int(x[2]))
64
+
65
+
66
+ def k_data2num_ks(kdata):
67
+ return int(kdata.split()[0])
68
+
69
+
70
+ def k_data2kpath(kdata):
71
+ return [float(x) for x in kdata.split()[1:7]]
72
+
73
+
74
+ def std_out_array(a):
75
+ return ''.join([str(x) + ' ' for x in a])
76
+
77
+
78
+ default_dtype = np.complex128
79
+
80
+ print(parsed_args.config)
81
+ with open(parsed_args.config) as f:
82
+ config = json.load(f)
83
+ calc_job = config["calc_job"]
84
+
85
+ if os.path.isfile(os.path.join(parsed_args.input_dir, "info.json")):
86
+ with open(os.path.join(parsed_args.input_dir, "info.json")) as f:
87
+ spinful = json.load(f)["isspinful"]
88
+ else:
89
+ spinful = False
90
+
91
+ site_positions = np.loadtxt(os.path.join(parsed_args.input_dir, "site_positions.dat"))
92
+
93
+ if len(site_positions.shape) == 2:
94
+ nsites = site_positions.shape[1]
95
+ else:
96
+ nsites = 1
97
+ # in case of single atom
98
+
99
+
100
+ with open(os.path.join(parsed_args.input_dir, "orbital_types.dat")) as f:
101
+ site_norbits = np.zeros(nsites, dtype=int)
102
+ orbital_types = []
103
+ for index_site in range(nsites):
104
+ orbital_type = list(map(int, f.readline().split()))
105
+ orbital_types.append(orbital_type)
106
+ site_norbits[index_site] = np.sum(np.array(orbital_type) * 2 + 1)
107
+ norbits = np.sum(site_norbits)
108
+ site_norbits_cumsum = np.cumsum(site_norbits)
109
+
110
+ rlat = np.loadtxt(os.path.join(parsed_args.input_dir, "rlat.dat")).T
111
+ # require transposition while reading rlat.dat in python
112
+
113
+
114
+ print("read h5")
115
+ begin_time = time()
116
+ hamiltonians_pred = _create_dict_h5(os.path.join(parsed_args.input_dir, "hamiltonians_pred.h5"))
117
+ overlaps = _create_dict_h5(os.path.join(parsed_args.input_dir, "overlaps.h5"))
118
+ print("Time for reading h5: ", time() - begin_time, "s")
119
+
120
+ H_R = {}
121
+ S_R = {}
122
+
123
+ print("construct Hamiltonian and overlap matrix in the real space")
124
+ begin_time = time()
125
+
126
+ # BS:
127
+ # this is for debug python and julia
128
+ # in julia, you can use 'sort(collect(keys(hamiltonians_pred)))'
129
+ # for key in dict(sorted(hamiltonians_pred.items())).keys():
130
+ for key in hamiltonians_pred.keys():
131
+
132
+ hamiltonian_pred = hamiltonians_pred[key]
133
+
134
+ if key in overlaps.keys():
135
+ overlap = overlaps[key]
136
+ else:
137
+ overlap = np.zeros_like(hamiltonian_pred)
138
+ if spinful:
139
+ overlap = np.vstack((np.hstack((overlap, np.zeros_like(overlap))), np.hstack((np.zeros_like(overlap), overlap))))
140
+ R = key[:3]
141
+ atom_i = key[3] - 1
142
+ atom_j = key[4] - 1
143
+
144
+ assert (site_norbits[atom_i], site_norbits[atom_j]) == hamiltonian_pred.shape
145
+ assert (site_norbits[atom_i], site_norbits[atom_j]) == overlap.shape
146
+
147
+ if R not in H_R.keys():
148
+ H_R[R] = np.zeros((norbits, norbits), dtype=default_dtype)
149
+ S_R[R] = np.zeros((norbits, norbits), dtype=default_dtype)
150
+
151
+ for block_matrix_i in range(1, site_norbits[atom_i]+1):
152
+ for block_matrix_j in range(1, site_norbits[atom_j]+1):
153
+ index_i = site_norbits_cumsum[atom_i] - site_norbits[atom_i] + block_matrix_i - 1
154
+ index_j = site_norbits_cumsum[atom_j] - site_norbits[atom_j] + block_matrix_j - 1
155
+ H_R[R][index_i, index_j] = hamiltonian_pred[block_matrix_i-1, block_matrix_j-1]
156
+ S_R[R][index_i, index_j] = overlap[block_matrix_i-1, block_matrix_j-1]
157
+
158
+
159
+ print("Time for constructing Hamiltonian and overlap matrix in the real space: ", time() - begin_time, " s")
160
+
161
+ if calc_job == "band":
162
+ fermi_level = config["fermi_level"]
163
+ k_data = config["k_data"]
164
+ ill_project = parsed_args.ill_project or ("ill_project" in config.keys() and config["ill_project"])
165
+ ill_threshold = max(parsed_args.ill_threshold, config["ill_threshold"] if ("ill_threshold" in config.keys()) else 0.)
166
+ multiprocessing = max(parsed_args.multiprocessing, config["multiprocessing"] if ("multiprocessing" in config.keys()) else 0)
167
+
168
+ print("calculate bands")
169
+ num_ks = [k_data2num_ks(k) for k in k_data]
170
+ kpaths = [k_data2kpath(k) for k in k_data]
171
+
172
+ egvals = np.zeros((norbits, sum(num_ks)))
173
+
174
+ begin_time = time()
175
+ idx_k = 0
176
+ # calculate total k points
177
+ total_num_ks = sum(num_ks)
178
+ list_index_kpath= []
179
+ list_index_kxyz=[]
180
+ for i in range(len(num_ks)):
181
+ list_index_kpath = list_index_kpath + ([i]*num_ks[i])
182
+ list_index_kxyz.extend(range(num_ks[i]))
183
+
184
+ def process_worker(k_point):
185
+ """ calculate band
186
+
187
+ Args:
188
+ k_point (int): the index of k point of all calculated k points
189
+
190
+ Returns:
191
+ json: {
192
+ "k_point":k_point,
193
+ "egval" (np array 1D) : eigen value ,
194
+ "num_projected_out" (int) : ill-conditioned eigenvalues detected。 default is 0
195
+ }
196
+ """
197
+ index_kpath = list_index_kpath[k_point]
198
+ kpath = kpaths[index_kpath]
199
+ pnkpts = num_ks[index_kpath]
200
+ kx = np.linspace(kpath[0], kpath[3], pnkpts)[list_index_kxyz[k_point]]
201
+ ky = np.linspace(kpath[1], kpath[4], pnkpts)[list_index_kxyz[k_point]]
202
+ kz = np.linspace(kpath[2], kpath[5], pnkpts)[list_index_kxyz[k_point]]
203
+
204
+ H_k = np.matrix(np.zeros((norbits, norbits), dtype=default_dtype))
205
+ S_k = np.matrix(np.zeros((norbits, norbits), dtype=default_dtype))
206
+ for R in H_R.keys():
207
+ H_k += H_R[R] * np.exp(1j*2*np.pi*np.dot([kx, ky, kz], R))
208
+ S_k += S_R[R] * np.exp(1j*2*np.pi*np.dot([kx, ky, kz], R))
209
+ # print(H_k)
210
+ H_k = (H_k + H_k.getH())/2.
211
+ S_k = (S_k + S_k.getH())/2.
212
+ num_projected_out = 0
213
+ if ill_project:
214
+ egval_S, egvec_S = linalg.eig(S_k)
215
+ project_index = np.argwhere(abs(egval_S)> ill_threshold)
216
+ if len(project_index) != norbits:
217
+ egvec_S = np.matrix(egvec_S[:, project_index])
218
+ num_projected_out = norbits - len(project_index)
219
+ H_k = egvec_S.H @ H_k @ egvec_S
220
+ S_k = egvec_S.H @ S_k @ egvec_S
221
+ egval = linalg.eigvalsh(H_k, S_k, lower=False)
222
+ egval = np.concatenate([egval, np.full(num_projected_out, 1e4)])
223
+ else:
224
+ egval = linalg.eigvalsh(H_k, S_k, lower=False)
225
+ else:
226
+ #---------------------------------------------
227
+ # BS: only eigenvalues are needed in this part,
228
+ # the upper matrix is used
229
+ egval = linalg.eigvalsh(H_k, S_k, lower=False)
230
+
231
+ return {"k_point":k_point, "egval":egval, "num_projected_out":num_projected_out}
232
+
233
+ # parallizing the band calculation
234
+ if multiprocessing == 0:
235
+ print(f'No use of multiprocessing')
236
+ data_list = [process_worker(k_point) for k_point in tqdm.tqdm(range(sum(num_ks)))]
237
+ else:
238
+ pool_dict = {} if multiprocessing < 0 else {'nodes': multiprocessing}
239
+
240
+ with Pool(**pool_dict) as pool:
241
+ nodes = pool.nodes
242
+ print(f'Use multiprocessing x {multiprocessing})')
243
+ data_list = list(tqdm.tqdm(pool.imap(process_worker, range(sum(num_ks))), total=sum(num_ks)))
244
+
245
+ # post-process returned band data, and store them in egvals with the order k_point
246
+ projected_out = []
247
+ for data in data_list:
248
+ egvals[:, data["k_point"]] = data["egval"]
249
+ if data["num_projected_out"] > 0:
250
+ projected_out.append(data["num_projected_out"])
251
+ if len(projected_out) > 0:
252
+ print(f"There are {len(projected_out)} bands with ill-conditioned eigenvalues detected.")
253
+ print(f"Projected out {int(np.average(projected_out))} eigenvalues on average.")
254
+ print('Finish the calculation of %d k-points, have cost %d seconds' % (sum(num_ks), time() - begin_time))
255
+
256
+
257
+ # output in openmx band format
258
+ with open(os.path.join(parsed_args.output_dir, "openmx.Band"), "w") as f:
259
+ f.write("{} {} {}\n".format(norbits, 0, ev2Hartree * fermi_level))
260
+ openmx_rlat = np.reshape((rlat * Bohr2Ang), (1, -1))[0]
261
+ f.write(std_out_array(openmx_rlat) + "\n")
262
+ f.write(str(len(k_data)) + "\n")
263
+ for line in k_data:
264
+ f.write(line + "\n")
265
+ idx_k = 0
266
+ for i in range(len(kpaths)):
267
+ pnkpts = num_ks[i]
268
+ kstart = kpaths[i][:3]
269
+ kend = kpaths[i][3:]
270
+ k_list = np.zeros((pnkpts, 3))
271
+ for alpha in range(3):
272
+ k_list[:, alpha] = genlist([kstart[alpha], kend[alpha], pnkpts])
273
+ for j in range(pnkpts):
274
+ kvec = k_list[j, :]
275
+ f.write("{} {}\n".format(norbits, std_out_array(kvec)))
276
+ f.write(std_out_array(ev2Hartree * egvals[:, idx_k]) + "\n")
277
+ idx_k += 1
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/inference/inference_default.ini ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [basic]
2
+ work_dir = /your/own/path
3
+ OLP_dir = /your/own/path
4
+ interface = openmx
5
+ trained_model_dir = ["/your/trained/model1", "/your/trained/model2"]
6
+ task = [1, 2, 3, 4, 5]
7
+ sparse_calc_config = /your/own/path
8
+ eigen_solver = sparse_jl
9
+ disable_cuda = True
10
+ device = cuda:0
11
+ huge_structure = True
12
+ restore_blocks_py = True
13
+ gen_rc_idx = False
14
+ gen_rc_by_idx =
15
+ with_grad = False
16
+
17
+ [interpreter]
18
+ julia_interpreter = julia
19
+ python_interpreter = python
20
+
21
+ [graph]
22
+ radius = -1.0
23
+ create_from_DFT = True
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/inference/local_coordinate.jl ADDED
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ using DelimitedFiles, LinearAlgebra
2
+ using HDF5
3
+ using ArgParse
4
+ using StaticArrays
5
+
6
+
7
+ function parse_commandline()
8
+ s = ArgParseSettings()
9
+ @add_arg_table! s begin
10
+ "--input_dir", "-i"
11
+ help = "path of site_positions.dat, lat.dat, element.dat, and R_list.dat (overlaps.h5)"
12
+ arg_type = String
13
+ default = "./"
14
+ "--output_dir", "-o"
15
+ help = "path of output rc.h5"
16
+ arg_type = String
17
+ default = "./"
18
+ "--radius", "-r"
19
+ help = "cutoff radius"
20
+ arg_type = Float64
21
+ default = 8.0
22
+ "--create_from_DFT"
23
+ help = "retain edges by DFT overlaps neighbour"
24
+ arg_type = Bool
25
+ default = true
26
+ "--output_text"
27
+ help = "an option without argument, i.e. a flag"
28
+ action = :store_true
29
+ "--Hop_dir"
30
+ help = "path of Hop.jl"
31
+ arg_type = String
32
+ default = "/home/lihe/DeepH/process_ham/Hop.jl/"
33
+ end
34
+ return parse_args(s)
35
+ end
36
+ parsed_args = parse_commandline()
37
+
38
+ using Pkg
39
+ Pkg.activate(parsed_args["Hop_dir"])
40
+ using Hop
41
+
42
+
43
+ site_positions = readdlm(joinpath(parsed_args["input_dir"], "site_positions.dat"))
44
+ lat = readdlm(joinpath(parsed_args["input_dir"], "lat.dat"))
45
+ R_list_read = convert(Matrix{Int64}, readdlm(joinpath(parsed_args["input_dir"], "R_list.dat")))
46
+ num_R = size(R_list_read, 1)
47
+ R_list = Vector{SVector{3, Int64}}()
48
+ for index_R in 1:num_R
49
+ push!(R_list, SVector{3, Int64}(R_list_read[index_R, :]))
50
+ end
51
+
52
+ @info "get local coordinate"
53
+ begin_time = time()
54
+ rcoordinate = Hop.Deeph.rotate_system(site_positions, lat, R_list, parsed_args["radius"])
55
+ println("time for calculating local coordinate is: ", time() - begin_time)
56
+
57
+ if parsed_args["output_text"]
58
+ @info "output txt"
59
+ mkpath(joinpath(parsed_args["output_dir"], "rresult"))
60
+ mkpath(joinpath(parsed_args["output_dir"], "rresult/rc"))
61
+ for (R, coord) in rcoordinate
62
+ open(joinpath(parsed_args["output_dir"], "rresult/rc/", R, "_real.dat"), "w") do f
63
+ writedlm(f, coord)
64
+ end
65
+ end
66
+ end
67
+
68
+ @info "output h5"
69
+ h5open(joinpath(parsed_args["input_dir"], "overlaps.h5"), "r") do fid_OLP
70
+ graph_key = Set(keys(fid_OLP))
71
+ h5open(joinpath(parsed_args["output_dir"], "rc.h5"), "w") do fid
72
+ for (key, coord) in rcoordinate
73
+ if (parsed_args["create_from_DFT"] == true) && (!(string(key) in graph_key))
74
+ continue
75
+ end
76
+ write(fid, string(key), permutedims(coord))
77
+ end
78
+ end
79
+ end
3_epc/displacements/group_8/reconstruction/aohamiltonian/pred_ham_std/src/deeph/inference/pred_ham.py ADDED
@@ -0,0 +1,365 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json
2
+ import os
3
+ import time
4
+ import warnings
5
+ from typing import Union, List
6
+ import sys
7
+
8
+ import tqdm
9
+ from configparser import ConfigParser
10
+ import numpy as np
11
+ from pymatgen.core.structure import Structure
12
+ import torch
13
+ import torch.autograd.forward_ad as fwAD
14
+ import h5py
15
+
16
+ from deeph import get_graph, DeepHKernel, collate_fn, write_ham_h5, load_orbital_types, Rotate, dtype_dict, get_rc
17
+
18
+
19
+ def predict(input_dir: str, output_dir: str, disable_cuda: bool, device: str,
20
+ huge_structure: bool, restore_blocks_py: bool, trained_model_dirs: Union[str, List[str]]):
21
+ atom_num_orbital = load_orbital_types(os.path.join(input_dir, 'orbital_types.dat'))
22
+ if isinstance(trained_model_dirs, str):
23
+ trained_model_dirs = [trained_model_dirs]
24
+ assert isinstance(trained_model_dirs, list)
25
+ os.makedirs(output_dir, exist_ok=True)
26
+ predict_spinful = None
27
+
28
+ with torch.no_grad():
29
+ read_structure_flag = False
30
+ if restore_blocks_py:
31
+ hoppings_pred = {}
32
+ else:
33
+ index_model = 0
34
+ block_without_restoration = {}
35
+ os.makedirs(os.path.join(output_dir, 'block_without_restoration'), exist_ok=True)
36
+ for trained_model_dir in tqdm.tqdm(trained_model_dirs):
37
+ old_version = False
38
+ assert os.path.exists(os.path.join(trained_model_dir, 'config.ini'))
39
+ if os.path.exists(os.path.join(trained_model_dir, 'best_model.pt')) is False:
40
+ old_version = True
41
+ assert os.path.exists(os.path.join(trained_model_dir, 'best_model.pkl'))
42
+ assert os.path.exists(os.path.join(trained_model_dir, 'src'))
43
+
44
+ config = ConfigParser()
45
+ config.read(os.path.join(os.path.dirname(os.path.dirname(__file__)), 'default.ini'))
46
+ config.read(os.path.join(trained_model_dir, 'config.ini'))
47
+ config.set('basic', 'save_dir', os.path.join(output_dir, 'pred_ham_std'))
48
+ config.set('basic', 'disable_cuda', str(disable_cuda))
49
+ config.set('basic', 'device', str(device))
50
+ config.set('basic', 'save_to_time_folder', 'False')
51
+ config.set('basic', 'tb_writer', 'False')
52
+ config.set('train', 'pretrained', '')
53
+ config.set('train', 'resume', '')
54
+
55
+ kernel = DeepHKernel(config)
56
+ if old_version is False:
57
+ checkpoint = kernel.build_model(trained_model_dir, old_version)
58
+ else:
59
+ warnings.warn('You are using the trained model with an old version')
60
+ checkpoint = torch.load(
61
+ os.path.join(trained_model_dir, 'best_model.pkl'),
62
+ map_location=kernel.device
63
+ )
64
+ for key in ['index_to_Z', 'Z_to_index', 'spinful']:
65
+ if key in checkpoint:
66
+ setattr(kernel, key, checkpoint[key])
67
+ if hasattr(kernel, 'index_to_Z') is False:
68
+ kernel.index_to_Z = torch.arange(config.getint('basic', 'max_element') + 1)
69
+ if hasattr(kernel, 'Z_to_index') is False:
70
+ kernel.Z_to_index = torch.arange(config.getint('basic', 'max_element') + 1)
71
+ if hasattr(kernel, 'spinful') is False:
72
+ kernel.spinful = False
73
+ kernel.num_species = len(kernel.index_to_Z)
74
+ print("=> load best checkpoint (epoch {})".format(checkpoint['epoch']))
75
+ print(f"=> Atomic types: {kernel.index_to_Z.tolist()}, "
76
+ f"spinful: {kernel.spinful}, the number of atomic types: {len(kernel.index_to_Z)}.")
77
+ kernel.build_model(trained_model_dir, old_version)
78
+ kernel.model.load_state_dict(checkpoint['state_dict'])
79
+
80
+ if predict_spinful is None:
81
+ predict_spinful = kernel.spinful
82
+ else:
83
+ assert predict_spinful == kernel.spinful, "Different models' spinful are not compatible"
84
+
85
+ if read_structure_flag is False:
86
+ read_structure_flag = True
87
+ structure = Structure(np.loadtxt(os.path.join(input_dir, 'lat.dat')).T,
88
+ np.loadtxt(os.path.join(input_dir, 'element.dat')),
89
+ np.loadtxt(os.path.join(input_dir, 'site_positions.dat')).T,
90
+ coords_are_cartesian=True,
91
+ to_unit_cell=False)
92
+ cart_coords = torch.tensor(structure.cart_coords, dtype=torch.get_default_dtype())
93
+ frac_coords = torch.tensor(structure.frac_coords, dtype=torch.get_default_dtype())
94
+ numbers = kernel.Z_to_index[torch.tensor(structure.atomic_numbers)]
95
+ structure.lattice.matrix.setflags(write=True)
96
+ lattice = torch.tensor(structure.lattice.matrix, dtype=torch.get_default_dtype())
97
+ inv_lattice = torch.inverse(lattice)
98
+
99
+ if os.path.exists(os.path.join(input_dir, 'graph.pkl')):
100
+ data = torch.load(os.path.join(input_dir, 'graph.pkl'))
101
+ print(f"Load processed graph from {os.path.join(input_dir, 'graph.pkl')}")
102
+ else:
103
+ begin = time.time()
104
+ data = get_graph(cart_coords, frac_coords, numbers, 0,
105
+ r=kernel.config.getfloat('graph', 'radius'),
106
+ max_num_nbr=kernel.config.getint('graph', 'max_num_nbr'),
107
+ numerical_tol=1e-8, lattice=lattice, default_dtype_torch=torch.get_default_dtype(),
108
+ tb_folder=input_dir, interface="h5_rc_only",
109
+ num_l=kernel.config.getint('network', 'num_l'),
110
+ create_from_DFT=kernel.config.getboolean('graph', 'create_from_DFT',
111
+ fallback=True),
112
+ if_lcmp_graph=kernel.config.getboolean('graph', 'if_lcmp_graph', fallback=True),
113
+ separate_onsite=kernel.separate_onsite,
114
+ target=kernel.config.get('basic', 'target'), huge_structure=huge_structure,
115
+ if_new_sp=kernel.config.getboolean('graph', 'new_sp', fallback=False),
116
+ )
117
+ torch.save(data, os.path.join(input_dir, 'graph.pkl'))
118
+ print(
119
+ f"Save processed graph to {os.path.join(input_dir, 'graph.pkl')}, cost {time.time() - begin} seconds")
120
+ batch, subgraph = collate_fn([data])
121
+ sub_atom_idx, sub_edge_idx, sub_edge_ang, sub_index = subgraph
122
+
123
+ output = kernel.model(batch.x.to(kernel.device), batch.edge_index.to(kernel.device),
124
+ batch.edge_attr.to(kernel.device),
125
+ batch.batch.to(kernel.device),
126
+ sub_atom_idx.to(kernel.device), sub_edge_idx.to(kernel.device),
127
+ sub_edge_ang.to(kernel.device), sub_index.to(kernel.device),
128
+ huge_structure=huge_structure)
129
+ output = output.detach().cpu()
130
+ if restore_blocks_py:
131
+ for index in range(batch.edge_attr.shape[0]):
132
+ R = torch.round(batch.edge_attr[index, 4:7] @ inv_lattice - batch.edge_attr[index, 7:10] @ inv_lattice).int().tolist()
133
+ i, j = batch.edge_index[:, index]
134
+ key_term = (*R, i.item() + 1, j.item() + 1)
135
+ key_term = str(list(key_term))
136
+ for index_orbital, orbital_dict in enumerate(kernel.orbital):
137
+ if f'{kernel.index_to_Z[numbers[i]].item()} {kernel.index_to_Z[numbers[j]].item()}' not in orbital_dict:
138
+ continue
139
+ orbital_i, orbital_j = orbital_dict[f'{kernel.index_to_Z[numbers[i]].item()} {kernel.index_to_Z[numbers[j]].item()}']
140
+
141
+ if not key_term in hoppings_pred:
142
+ if kernel.spinful:
143
+ hoppings_pred[key_term] = np.full((2 * atom_num_orbital[i], 2 * atom_num_orbital[j]), np.nan + np.nan * (1j))
144
+ else:
145
+ hoppings_pred[key_term] = np.full((atom_num_orbital[i], atom_num_orbital[j]), np.nan)
146
+ if kernel.spinful:
147
+ hoppings_pred[key_term][orbital_i, orbital_j] = output[index][index_orbital * 8 + 0] + output[index][index_orbital * 8 + 1] * 1j
148
+ hoppings_pred[key_term][atom_num_orbital[i] + orbital_i, atom_num_orbital[j] + orbital_j] = output[index][index_orbital * 8 + 2] + output[index][index_orbital * 8 + 3] * 1j
149
+ hoppings_pred[key_term][orbital_i, atom_num_orbital[j] + orbital_j] = output[index][index_orbital * 8 + 4] + output[index][index_orbital * 8 + 5] * 1j
150
+ hoppings_pred[key_term][atom_num_orbital[i] + orbital_i, orbital_j] = output[index][index_orbital * 8 + 6] + output[index][index_orbital * 8 + 7] * 1j
151
+ else:
152
+ hoppings_pred[key_term][orbital_i, orbital_j] = output[index][index_orbital] # about output shape w/ or w/o soc, see graph.py line 164, and kernel.py line 281.
153
+ else:
154
+ if 'edge_index' not in block_without_restoration:
155
+ assert index_model == 0
156
+ block_without_restoration['edge_index'] = batch.edge_index
157
+ block_without_restoration['edge_attr'] = batch.edge_attr
158
+ block_without_restoration[f'output_{index_model}'] = output.numpy()
159
+ with open(os.path.join(output_dir, 'block_without_restoration', f'orbital_{index_model}.json'), 'w') as orbital_f:
160
+ json.dump(kernel.orbital, orbital_f, indent=4)
161
+ index_model += 1
162
+ sys.stdout = sys.stdout.terminal
163
+ sys.stderr = sys.stderr.terminal
164
+
165
+ if restore_blocks_py:
166
+ for hamiltonian in hoppings_pred.values():
167
+ assert np.all(np.isnan(hamiltonian) == False)
168
+ write_ham_h5(hoppings_pred, path=os.path.join(output_dir, 'rh_pred.h5'))
169
+ else:
170
+ block_without_restoration['num_model'] = index_model
171
+ write_ham_h5(block_without_restoration, path=os.path.join(output_dir, 'block_without_restoration', 'block_without_restoration.h5'))
172
+ with open(os.path.join(output_dir, "info.json"), 'w') as info_f:
173
+ json.dump({
174
+ "isspinful": predict_spinful
175
+ }, info_f)
176
+
177
+
178
+ def predict_with_grad(input_dir: str, output_dir: str, disable_cuda: bool, device: str,
179
+ huge_structure: bool, trained_model_dirs: Union[str, List[str]]):
180
+ atom_num_orbital, orbital_types = load_orbital_types(os.path.join(input_dir, 'orbital_types.dat'), return_orbital_types=True)
181
+
182
+ if isinstance(trained_model_dirs, str):
183
+ trained_model_dirs = [trained_model_dirs]
184
+ assert isinstance(trained_model_dirs, list)
185
+ os.makedirs(output_dir, exist_ok=True)
186
+ predict_spinful = None
187
+
188
+ read_structure_flag = False
189
+ rh_dict = {}
190
+ hamiltonians_pred = {}
191
+ hamiltonians_grad_pred = {}
192
+
193
+ for trained_model_dir in tqdm.tqdm(trained_model_dirs):
194
+ old_version = False
195
+ assert os.path.exists(os.path.join(trained_model_dir, 'config.ini'))
196
+ if os.path.exists(os.path.join(trained_model_dir, 'best_model.pt')) is False:
197
+ old_version = True
198
+ assert os.path.exists(os.path.join(trained_model_dir, 'best_model.pkl'))
199
+ assert os.path.exists(os.path.join(trained_model_dir, 'src'))
200
+
201
+ config = ConfigParser()
202
+ config.read(os.path.join(os.path.dirname(os.path.dirname(__file__)), 'default.ini'))
203
+ config.read(os.path.join(trained_model_dir, 'config.ini'))
204
+ config.set('basic', 'save_dir', os.path.join(output_dir, 'pred_ham_std'))
205
+ config.set('basic', 'disable_cuda', str(disable_cuda))
206
+ config.set('basic', 'device', str(device))
207
+ config.set('basic', 'save_to_time_folder', 'False')
208
+ config.set('basic', 'tb_writer', 'False')
209
+ config.set('train', 'pretrained', '')
210
+ config.set('train', 'resume', '')
211
+
212
+ kernel = DeepHKernel(config)
213
+ if old_version is False:
214
+ checkpoint = kernel.build_model(trained_model_dir, old_version)
215
+ else:
216
+ warnings.warn('You are using the trained model with an old version')
217
+ checkpoint = torch.load(
218
+ os.path.join(trained_model_dir, 'best_model.pkl'),
219
+ map_location=kernel.device
220
+ )
221
+ for key in ['index_to_Z', 'Z_to_index', 'spinful']:
222
+ if key in checkpoint:
223
+ setattr(kernel, key, checkpoint[key])
224
+ if hasattr(kernel, 'index_to_Z') is False:
225
+ kernel.index_to_Z = torch.arange(config.getint('basic', 'max_element') + 1)
226
+ if hasattr(kernel, 'Z_to_index') is False:
227
+ kernel.Z_to_index = torch.arange(config.getint('basic', 'max_element') + 1)
228
+ if hasattr(kernel, 'spinful') is False:
229
+ kernel.spinful = False
230
+ kernel.num_species = len(kernel.index_to_Z)
231
+ print("=> load best checkpoint (epoch {})".format(checkpoint['epoch']))
232
+ print(f"=> Atomic types: {kernel.index_to_Z.tolist()}, "
233
+ f"spinful: {kernel.spinful}, the number of atomic types: {len(kernel.index_to_Z)}.")
234
+ kernel.build_model(trained_model_dir, old_version)
235
+ kernel.model.load_state_dict(checkpoint['state_dict'])
236
+
237
+ if predict_spinful is None:
238
+ predict_spinful = kernel.spinful
239
+ else:
240
+ assert predict_spinful == kernel.spinful, "Different models' spinful are not compatible"
241
+
242
+ if read_structure_flag is False:
243
+ read_structure_flag = True
244
+ structure = Structure(np.loadtxt(os.path.join(input_dir, 'lat.dat')).T,
245
+ np.loadtxt(os.path.join(input_dir, 'element.dat')),
246
+ np.loadtxt(os.path.join(input_dir, 'site_positions.dat')).T,
247
+ coords_are_cartesian=True,
248
+ to_unit_cell=False)
249
+ cart_coords = torch.tensor(structure.cart_coords, dtype=torch.get_default_dtype(), requires_grad=True, device=kernel.device)
250
+ num_atom = cart_coords.shape[0]
251
+ frac_coords = torch.tensor(structure.frac_coords, dtype=torch.get_default_dtype())
252
+ numbers = kernel.Z_to_index[torch.tensor(structure.atomic_numbers)]
253
+ structure.lattice.matrix.setflags(write=True)
254
+ lattice = torch.tensor(structure.lattice.matrix, dtype=torch.get_default_dtype())
255
+ inv_lattice = torch.inverse(lattice)
256
+
257
+ fid_rc = get_rc(input_dir, None, radius=-1, create_from_DFT=True, if_require_grad=True, cart_coords=cart_coords)
258
+
259
+ assert kernel.config.getboolean('graph', 'new_sp', fallback=False)
260
+ data = get_graph(cart_coords.to(kernel.device), frac_coords, numbers, 0,
261
+ r=kernel.config.getfloat('graph', 'radius'),
262
+ max_num_nbr=kernel.config.getint('graph', 'max_num_nbr'),
263
+ numerical_tol=1e-8, lattice=lattice, default_dtype_torch=torch.get_default_dtype(),
264
+ tb_folder=input_dir, interface="h5_rc_only",
265
+ num_l=kernel.config.getint('network', 'num_l'),
266
+ create_from_DFT=kernel.config.getboolean('graph', 'create_from_DFT', fallback=True),
267
+ if_lcmp_graph=kernel.config.getboolean('graph', 'if_lcmp_graph', fallback=True),
268
+ separate_onsite=kernel.separate_onsite,
269
+ target=kernel.config.get('basic', 'target'), huge_structure=huge_structure,
270
+ if_new_sp=True, if_require_grad=True, fid_rc=fid_rc)
271
+ batch, subgraph = collate_fn([data])
272
+ sub_atom_idx, sub_edge_idx, sub_edge_ang, sub_index = subgraph
273
+
274
+ torch_dtype, torch_dtype_real, torch_dtype_complex = dtype_dict[torch.get_default_dtype()]
275
+ rotate_kernel = Rotate(torch_dtype, torch_dtype_real=torch_dtype_real,
276
+ torch_dtype_complex=torch_dtype_complex,
277
+ device=kernel.device, spinful=kernel.spinful)
278
+
279
+ output = kernel.model(batch.x, batch.edge_index.to(kernel.device),
280
+ batch.edge_attr,
281
+ batch.batch.to(kernel.device),
282
+ sub_atom_idx.to(kernel.device), sub_edge_idx.to(kernel.device),
283
+ sub_edge_ang, sub_index.to(kernel.device),
284
+ huge_structure=huge_structure)
285
+
286
+ index_for_matrix_block_real_dict = {} # key is atomic number pair
287
+ if kernel.spinful:
288
+ index_for_matrix_block_imag_dict = {} # key is atomic number pair
289
+
290
+ for index in range(batch.edge_attr.shape[0]):
291
+ R = torch.round(batch.edge_attr[index, 4:7].cpu() @ inv_lattice - batch.edge_attr[index, 7:10].cpu() @ inv_lattice).int().tolist()
292
+ i, j = batch.edge_index[:, index]
293
+ key_tensor = torch.tensor([*R, i, j])
294
+ numbers_pair = (kernel.index_to_Z[numbers[i]].item(), kernel.index_to_Z[numbers[j]].item())
295
+ if numbers_pair not in index_for_matrix_block_real_dict:
296
+ if not kernel.spinful:
297
+ index_for_matrix_block_real = torch.full((atom_num_orbital[i], atom_num_orbital[j]), -1)
298
+ else:
299
+ index_for_matrix_block_real = torch.full((2 * atom_num_orbital[i], 2 * atom_num_orbital[j]), -1)
300
+ index_for_matrix_block_imag = torch.full((2 * atom_num_orbital[i], 2 * atom_num_orbital[j]), -1)
301
+ for index_orbital, orbital_dict in enumerate(kernel.orbital):
302
+ if f'{kernel.index_to_Z[numbers[i]].item()} {kernel.index_to_Z[numbers[j]].item()}' not in orbital_dict:
303
+ continue
304
+ orbital_i, orbital_j = orbital_dict[f'{kernel.index_to_Z[numbers[i]].item()} {kernel.index_to_Z[numbers[j]].item()}']
305
+ if not kernel.spinful:
306
+ index_for_matrix_block_real[orbital_i, orbital_j] = index_orbital
307
+ else:
308
+ index_for_matrix_block_real[orbital_i, orbital_j] = index_orbital * 8 + 0
309
+ index_for_matrix_block_imag[orbital_i, orbital_j] = index_orbital * 8 + 1
310
+ index_for_matrix_block_real[atom_num_orbital[i] + orbital_i, atom_num_orbital[j] + orbital_j] = index_orbital * 8 + 2
311
+ index_for_matrix_block_imag[atom_num_orbital[i] + orbital_i, atom_num_orbital[j] + orbital_j] = index_orbital * 8 + 3
312
+ index_for_matrix_block_real[orbital_i, atom_num_orbital[j] + orbital_j] = index_orbital * 8 + 4
313
+ index_for_matrix_block_imag[orbital_i, atom_num_orbital[j] + orbital_j] = index_orbital * 8 + 5
314
+ index_for_matrix_block_real[atom_num_orbital[i] + orbital_i, orbital_j] = index_orbital * 8 + 6
315
+ index_for_matrix_block_imag[atom_num_orbital[i] + orbital_i, orbital_j] = index_orbital * 8 + 7
316
+ assert torch.all(index_for_matrix_block_real != -1), 'json string "orbital" should be complete for Hamiltonian grad'
317
+ if kernel.spinful:
318
+ assert torch.all(index_for_matrix_block_imag != -1), 'json string "orbital" should be complete for Hamiltonian grad'
319
+
320
+ index_for_matrix_block_real_dict[numbers_pair] = index_for_matrix_block_real
321
+ if kernel.spinful:
322
+ index_for_matrix_block_imag_dict[numbers_pair] = index_for_matrix_block_imag
323
+ else:
324
+ index_for_matrix_block_real = index_for_matrix_block_real_dict[numbers_pair]
325
+ if kernel.spinful:
326
+ index_for_matrix_block_imag = index_for_matrix_block_imag_dict[numbers_pair]
327
+
328
+ if not kernel.spinful:
329
+ rh_dict[key_tensor] = output[index][index_for_matrix_block_real]
330
+ else:
331
+ rh_dict[key_tensor] = output[index][index_for_matrix_block_real] + 1j * output[index][index_for_matrix_block_imag]
332
+
333
+ sys.stdout = sys.stdout.terminal
334
+ sys.stderr = sys.stderr.terminal
335
+
336
+ print("=> Hamiltonian has been predicted, calculate the grad...")
337
+ for key_tensor, rotated_hamiltonian in tqdm.tqdm(rh_dict.items()):
338
+ atom_i = key_tensor[3]
339
+ atom_j = key_tensor[4]
340
+ assert atom_i >= 0
341
+ assert atom_i < num_atom
342
+ assert atom_j >= 0
343
+ assert atom_j < num_atom
344
+ key_str = str(list([key_tensor[0].item(), key_tensor[1].item(), key_tensor[2].item(), atom_i.item() + 1, atom_j.item() + 1]))
345
+ assert key_str in fid_rc, f'Can not found the key "{key_str}" in rc.h5'
346
+ # rotation_matrix = torch.tensor(fid_rc[key_str], dtype=torch_dtype_real, device=kernel.device).T
347
+ rotation_matrix = fid_rc[key_str].T
348
+ hamiltonian = rotate_kernel.rotate_openmx_H(rotated_hamiltonian, rotation_matrix, orbital_types[atom_i], orbital_types[atom_j])
349
+ hamiltonians_pred[key_str] = hamiltonian.detach().cpu()
350
+ assert kernel.spinful is False # 检查soc时是否正确
351
+ assert len(hamiltonian.shape) == 2
352
+ dim_1, dim_2 = hamiltonian.shape[:]
353
+ assert key_str not in hamiltonians_grad_pred
354
+ if not kernel.spinful:
355
+ hamiltonians_grad_pred[key_str] = np.full((dim_1, dim_2, num_atom, 3), np.nan)
356
+ else:
357
+ hamiltonians_grad_pred[key_str] = np.full((2 * dim_1, 2 * dim_2, num_atom, 3), np.nan + 1j * np.nan)
358
+
359
+ write_ham_h5(hamiltonians_pred, path=os.path.join(output_dir, 'hamiltonians_pred.h5'))
360
+ write_ham_h5(hamiltonians_grad_pred, path=os.path.join(output_dir, 'hamiltonians_grad_pred.h5'))
361
+ with open(os.path.join(output_dir, "info.json"), 'w') as info_f:
362
+ json.dump({
363
+ "isspinful": predict_spinful
364
+ }, info_f)
365
+ fid_rc.close()