#!/usr/bin/env julia #= EPC with ML/HPRO AO brackets for diamond + comparison plots. Loads precomputed AO brackets from ml_epc.py and runs EPC using ElectronPhonon.jl with ML/HPRO eigenvectors instead of DFT wavefunctions. Compares results against DFT EPC from diamond.jl (out_dft/). Prerequisites: 1. diamond.jl (create + run + prepare + calc_ep) → displacements/out_dft/ 2. ml_epc.py (all steps) → displacements/scf_0/ao_brackets_{hpro,e3}.npz Usage: cd example/diamond/3_epc julia diamond_ml.jl =# using ElectronPhonon, PythonCall, NPZ, Printf, Statistics # ============================================================ # Paths (same model as diamond.jl) # ============================================================ SCRIPT_DIR = @__DIR__ path_to_calc = SCRIPT_DIR * "/" path_to_qe = "/home/apolyukhin/Development/q-e_tmp/" mpi_ranks = 8 a = 3.567 sc_size = [1, 1, 1] k_mesh = [6, 6, 6] natoms = 2 Ndisplace = 6 * natoms pseudo_dir = SCRIPT_DIR * "/../pseudos/" unitcell = Dict( :symbols => pylist(["C", "C"]), :cell => pylist([ [0.0, a/2, a/2], [a/2, 0.0, a/2], [a/2, a/2, 0.0] ]), :scaled_positions => pylist([ (0.0, 0.0, 0.0), (0.25, 0.25, 0.25) ]), :masses => pylist([12.011, 12.011]), ) scf_parameters = Dict( :format => "espresso-in", :kpts => pytuple((k_mesh[1], k_mesh[2], k_mesh[3])), :calculation => "scf", :prefix => "scf", :outdir => "./tmp/", :pseudo_dir => pseudo_dir, :ecutwfc => 60, :conv_thr => 1.0e-13, :pseudopotentials => Dict("C" => "C.upf"), :diagonalization => "david", :mixing_mode => "plain", :mixing_beta => 0.7, :crystal_coordinates => true, :verbosity => "high", :tstress => false, :ibrav => 0, :tprnfor => true, :nbnd => 8, :electron_maxstep => 1000, :nosym => true, :noinv => true, ) model = create_model( path_to_calc = path_to_calc, abs_disp = 1e-3, path_to_qe = path_to_qe, mpi_ranks = mpi_ranks, sc_size = sc_size, k_mesh = k_mesh, Ndispalce = Ndisplace, unitcell = unitcell, scf_parameters = scf_parameters, use_symm = false, ) # Load DFT electrons/phonons (for k_list and phonons reference) println("Loading DFT electrons and phonons...") electrons_dft = load_electrons(model) phonons = load_phonons(model) disp_dir = path_to_calc * "displacements/" scf0_dir = disp_dir * "scf_0/" # ============================================================ # Helper: load AO brackets from npz # ============================================================ function load_ao_brackets(npz_file) data = npzread(npz_file) U_raw = data["U_list"] # (n_dirs, nk, nk, nbands, nbands) complex128 V_raw = data["V_list"] ek_raw = data["ek_list"] # (nk, nbands) ep_raw = data["ep_list"] # (n_dirs, nk, nbands) epm_raw = data["epm_list"] ndisp = size(U_raw, 1) nk = size(U_raw, 2) U_list = [U_raw[d, :, :, :, :] for d in 1:ndisp] V_list = [V_raw[d, :, :, :, :] for d in 1:ndisp] ek_list = [ek_raw[ik, :] for ik in 1:nk] ep_list = [[ep_raw[d, ik, :] for ik in 1:nk] for d in 1:ndisp] epm_list = [[epm_raw[d, ik, :] for ik in 1:nk] for d in 1:ndisp] return U_list, V_list, ek_list, ep_list, epm_list end # ============================================================ # Helper: run EPC for one source and save to out_{source}_ao/ # ============================================================ function run_epc_ao(source::String) npz_file = scf0_dir * "ao_brackets_$(source).npz" if !isfile(npz_file) println("WARNING: $(npz_file) not found — run ml_epc.py first") return false end println("\n" * "="^60) println("Running EPC with AO brackets from $(uppercase(source))") println("="^60) U_list, V_list, ek_list, ep_list, epm_list = load_ao_brackets(npz_file) println(" Displacements: $(length(U_list)), k-points: $(length(ek_list))") println(" Gamma eigenvalues (eV): ", round.(ek_list[1], digits=4)) nk_total = prod(k_mesh) out_dir = disp_dir * "out/" out_ao_dir = disp_dir * "out_$(source)_ao/" out_backup = disp_dir * "out_backup/" # Skip if output already complete if isdir(out_ao_dir) && length(readdir(out_ao_dir)) >= nk_total println(" Cached output found ($(length(readdir(out_ao_dir))) files) — skipping EPC run") return true end electrons_ao = Electrons( U_list, V_list, ek_list, ep_list, epm_list, electrons_dft.k_list, ) if isdir(out_dir) mv(out_dir, out_backup; force=true) end mkpath(out_dir) println("Calculating EPC for $(nk_total) k-points...") for ik in 1:nk_total electron_phonon(model, ik, 1, electrons_ao, phonons; phonons_dfpt=false) if ik % 50 == 0 println(" Done $(ik) / $(nk_total)") end end if isdir(out_ao_dir) rm(out_ao_dir; recursive=true) end mv(out_dir, out_ao_dir) if isdir(out_backup) mv(out_backup, out_dir) end println("Done! Files saved to: out_$(source)_ao/ ($(length(readdir(out_ao_dir))) files)") return true end # Run EPC for both sources run_epc_ao("hpro") run_epc_ao("e3") # ============================================================ # EPC comparison: parse comparison files # ============================================================ function parse_epc_dir(dir::String, nk::Int=216) g = Dict{Tuple{Int,Int,Int}, Float64}() for ik in 1:nk fn = joinpath(dir, "comparison_$(ik)_1.txt") isfile(fn) || continue open(fn) do f for line in eachline(f) cols = split(strip(line)) length(cols) >= 8 || continue i = parse(Int, cols[1]) j = parse(Int, cols[2]) nu = parse(Int, cols[3]) gv = parse(Float64, cols[8]) g[(i, j, nu)] = gv end end end return g end println("\n" * "="^60) println("EPC comparison: DFT vs HPRO_AO vs E3_AO") println("="^60) out_dft_dir = disp_dir * "out_dft/" out_hpro_dir = disp_dir * "out_hpro_ao/" out_e3_dir = disp_dir * "out_e3_ao/" nk = prod(k_mesh) nbands = 8 nmodes = 6 n_occ = 4 # occupied bands if !isdir(out_dft_dir) println("WARNING: out_dft/ not found — run diamond.jl calc_ep first") else g_dft = parse_epc_dir(out_dft_dir, nk) g_hpro = isdir(out_hpro_dir) ? parse_epc_dir(out_hpro_dir, nk) : Dict() g_e3 = isdir(out_e3_dir) ? parse_epc_dir(out_e3_dir, nk) : Dict() println(" DFT: $(length(g_dft)) elements") println(" HPRO_AO: $(length(g_hpro)) elements") println(" E3_AO: $(length(g_e3)) elements") # Per-mode MAE statistics @printf("\nPer-mode MAE (optical modes ν≥4, |g_DFT|>0.1 meV):\n") for nu in 4:nmodes errs_hpro = Float64[] errs_e3 = Float64[] for (key, gd) in g_dft key[3] == nu || continue abs(gd) > 1e-4 || continue if haskey(g_hpro, key) push!(errs_hpro, abs(g_hpro[key] - gd)) end if haskey(g_e3, key) push!(errs_e3, abs(g_e3[key] - gd)) end end n = length(errs_hpro) hpro_mae = n > 0 ? mean(errs_hpro) * 1000 : NaN e3_mae = length(errs_e3) > 0 ? mean(errs_e3) * 1000 : NaN @printf(" Mode %d: HPRO MAE=%.2f meV E3 MAE=%.2f meV (N=%d)\n", nu, hpro_mae, e3_mae, n) end # Diagonal vs off-diagonal breakdown (optical modes) for (label, src_g) in [("HPRO", g_hpro), ("E3", g_e3)] isempty(src_g) && continue diag_errs = Float64[] offdiag_errs = Float64[] vc_errs = Float64[] for (key, gd) in g_dft key[3] >= 4 || continue abs(gd) > 1e-4 || continue haskey(src_g, key) || continue i, j = key[1], key[2] err = abs(src_g[key] - gd) if i == j push!(diag_errs, err) elseif (i <= n_occ) != (j <= n_occ) push!(vc_errs, err) else push!(offdiag_errs, err) end end @printf("\n %s_AO vs DFT (optical, |g_DFT|>0.1 meV):\n", label) @printf(" occ-occ diagonal MAE = %.2f meV (N=%d)\n", isempty(diag_errs) ? NaN : mean(diag_errs)*1000, length(diag_errs)) @printf(" occ-occ off-diag MAE = %.2f meV (N=%d)\n", isempty(offdiag_errs) ? NaN : mean(offdiag_errs)*1000, length(offdiag_errs)) @printf(" occ-cond/cond-occ MAE = %.2f meV (N=%d)\n", isempty(vc_errs) ? NaN : mean(vc_errs)*1000, length(vc_errs)) all_errs = vcat(diag_errs, offdiag_errs, vc_errs) if !isempty(all_errs) @printf(" Overall optical MAE = %.2f meV (N=%d)\n", mean(all_errs)*1000, length(all_errs)) end end # ============================================================ # Comparison plots — write Python script to tempfile and run # ============================================================ println("\nGenerating comparison plots...") plot_path1 = SCRIPT_DIR * "/epc_comparison_ml.png" plot_path2 = SCRIPT_DIR * "/epc_per_kpoint_ml.png" plot_script = tempname() * ".py" write(plot_script, """ import sys, os import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt def parse_epc_dir(dir_path, nk=216): g = {} for ik in range(1, nk+1): fn = os.path.join(dir_path, f"comparison_{ik}_1.txt") if not os.path.isfile(fn): continue with open(fn) as f: for line in f: cols = line.split() if len(cols) < 8: continue i, j, nu = int(cols[0]), int(cols[1]), int(cols[2]) g[(ik, i, j, nu)] = float(cols[7]) return g def make_epc_plot(out_dft_dir, ml_dirs, labels, n_occ=4, nk=216, out_path="epc_comparison.png"): \"\"\"Scatter plot: 3 columns (occ-occ / cond-cond / occ-cond) per ML method row.\"\"\" g_dft = parse_epc_dir(out_dft_dir, nk) optical_keys = [k for k in g_dft if k[3] >= 4 and abs(g_dft[k]) > 1e-4] g_dft_arr = np.array([g_dft[k] for k in optical_keys]) * 1000 # meV is_vv = np.array([k[1] <= n_occ and k[2] <= n_occ for k in optical_keys]) is_cc = np.array([k[1] > n_occ and k[2] > n_occ for k in optical_keys]) is_vc = ~is_vv & ~is_cc cats = [('occ-occ', is_vv, 'C0'), ('cond-cond', is_cc, 'C1'), ('occ-cond', is_vc, 'C2')] nrows, ncols = len(ml_dirs), 3 fig, axes = plt.subplots(nrows, ncols, figsize=(5*ncols, 5*nrows), squeeze=False) for row, (ml_dir, ml_label) in enumerate(zip(ml_dirs, labels)): if not os.path.isdir(ml_dir): continue g_ml = parse_epc_dir(ml_dir, nk) g_ml_arr = np.array([g_ml.get(k, 0.0) for k in optical_keys]) * 1000 for col, (cat_label, mask, color) in enumerate(cats): ax = axes[row, col] if mask.sum() == 0: ax.set_visible(False); continue gd = g_dft_arr[mask]; gm = g_ml_arr[mask] mae = np.mean(np.abs(gm - gd)) lim = max(abs(gd).max(), abs(gm).max()) * 1.05 ax.scatter(gd, gm, s=4, alpha=0.3, c=color, rasterized=True) ax.plot([-lim, lim], [-lim, lim], 'k--', lw=0.8, alpha=0.6) ax.set_xlim(-lim, lim); ax.set_ylim(-lim, lim); ax.set_aspect('equal') ax.set_xlabel('DFT g (meV)', fontsize=9) ax.set_ylabel(f'{ml_label} g (meV)', fontsize=9) ax.set_title(f'{ml_label} — {cat_label}\\nMAE={mae:.1f} meV N={mask.sum()}', fontsize=9) plt.tight_layout() plt.savefig(out_path, dpi=150, bbox_inches='tight') plt.close(fig) print(f' Saved: {out_path}') def make_per_kpoint_plot(out_dft_dir, ml_dirs, labels, n_occ=4, nk=216, out_path="epc_per_kpoint.png"): \"\"\"Stacked bar chart: per-k-point MAE for occ-occ / cond-cond / occ-cond.\"\"\" g_dft = parse_epc_dir(out_dft_dir, nk) optical_keys = [k for k in g_dft if k[3] >= 4 and abs(g_dft[k]) > 1e-4] g_dft_arr = np.array([g_dft[k] for k in optical_keys]) * 1000 is_vv = np.array([k[1] <= n_occ and k[2] <= n_occ for k in optical_keys]) is_cc = np.array([k[1] > n_occ and k[2] > n_occ for k in optical_keys]) is_vc = ~is_vv & ~is_cc ik_arr = np.array([k[0] for k in optical_keys]) k_idx = np.arange(1, nk + 1) fig, axes = plt.subplots(len(ml_dirs), 1, figsize=(14, 4.5 * len(ml_dirs)), squeeze=False) for row, (ml_dir, ml_label) in enumerate(zip(ml_dirs, labels)): if not os.path.isdir(ml_dir): continue g_ml = parse_epc_dir(ml_dir, nk) g_ml_arr = np.array([g_ml.get(k, 0.0) for k in optical_keys]) * 1000 def per_k_mae(mask): out = np.zeros(nk) for ik in k_idx: sel = (ik_arr == ik) & mask if sel.any(): out[ik - 1] = np.mean(np.abs(g_ml_arr[sel] - g_dft_arr[sel])) return out vv_mae = per_k_mae(is_vv) cc_mae = per_k_mae(is_cc) vc_mae = per_k_mae(is_vc) avg_vv = vv_mae[vv_mae > 0].mean() if vv_mae.any() else 0.0 avg_cc = cc_mae[cc_mae > 0].mean() if cc_mae.any() else 0.0 avg_vc = vc_mae[vc_mae > 0].mean() if vc_mae.any() else 0.0 ax = axes[row, 0] ax.bar(k_idx, vv_mae, color='C0', label=f'val-val (avg={avg_vv:.1f})', width=1.0) ax.bar(k_idx, cc_mae, bottom=vv_mae, color='C1', label=f'cond-cond (avg={avg_cc:.1f})', width=1.0) ax.bar(k_idx, vc_mae, bottom=vv_mae + cc_mae, color='C2', label=f'val-cond (avg={avg_vc:.1f})', width=1.0) ax.set_xlabel('k-point index', fontsize=10) ax.set_ylabel('MAE (meV)', fontsize=10) ax.set_title(f'{ml_label}: Per k-point EPC MAE', fontsize=11) ax.legend(fontsize=9, loc='upper right') plt.tight_layout() plt.savefig(out_path, dpi=150, bbox_inches='tight') plt.close(fig) print(f' Saved: {out_path}') if __name__ == "__main__": out_dft_dir, out_hpro_dir, out_e3_dir, path1, path2 = sys.argv[1:6] make_epc_plot(out_dft_dir, [out_hpro_dir, out_e3_dir], ['HPRO_AO', 'E3_AO'], out_path=path1) make_per_kpoint_plot(out_dft_dir, [out_hpro_dir, out_e3_dir], ['HPRO_AO', 'E3_AO'], out_path=path2) """) try # Use epc_ml conda env python; fall back to Sys.which (honours PATH from conda run) python_exe = let p = expanduser("~/anaconda3/envs/epc_ml/bin/python") isfile(p) ? p : something(Sys.which("python"), "python") end run(`$python_exe $plot_script $out_dft_dir $out_hpro_dir $out_e3_dir $plot_path1 $plot_path2`) finally rm(plot_script; force=true) end println("\ndiamond_ml.jl done.") println(" epc_comparison_ml.png — scatter: DFT vs HPRO_AO vs E3_AO") println(" epc_error_dist_ml.png — error histograms per band category") end