initial release: TRIP50 v1.0.0 dataset, scripts, examples

CITATION.cff

@@ -0,0 +1,31 @@
+cff-version: 1.2.0
+message: "If you use the TRIP50 benchmark, please cite the original publication."
+type: dataset
+title: "TRIP50: a benchmark of triplet-state organic reactions"
+authors:
+  - family-names: "Hughes"
+    given-names: "William B."
+  - family-names: "Popescu"
+    given-names: "Mihai V."
+  - family-names: "Paton"
+    given-names: "Robert S."
+doi: "10.1021/acs.jctc.6c00144"
+year: 2026
+url: "https://huggingface.co/datasets/patonlab/trip50"
+license: CC-BY-4.0
+preferred-citation:
+  type: article
+  title: "Fundamental Study of Density Functional Theory Applied to Triplet State Reactivity: Introduction of the TRIP50 Data Set"
+  authors:
+    - family-names: "Hughes"
+      given-names: "William B."
+    - family-names: "Popescu"
+      given-names: "Mihai V."
+    - family-names: "Paton"
+      given-names: "Robert S."
+  journal: "Journal of Chemical Theory and Computation"
+  volume: 22
+  start: 3530
+  end: 3542
+  doi: "10.1021/acs.jctc.6c00144"
+  year: 2026

LICENSE

@@ -0,0 +1,396 @@
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README.md

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+---
+license: cc-by-4.0
+pretty_name: TRIP50 Reaction-Energy Benchmark
+size_categories:
+  - n<1K
+task_categories:
+  - tabular-regression
+tags:
+  - chemistry
+  - mlip
+  - benchmark
+  - dft
+  - coupled-cluster
+  - reaction-energy
+  - transition-state
+  - radical
+  - triplet
+  - ase
+language:
+  - en
+configs:
+  - config_name: species
+    data_files: data/species.parquet
+  - config_name: reactions
+    data_files: data/reactions.parquet
+  - config_name: aliases
+    data_files: data/aliases.parquet
+---
+
+# TRIP50
+
+TRIP50 is a 50-reaction benchmark for radical and triplet thermochemistry and forward/reverse barriers, with a DLPNO-CCSD(T) reference and 46 DFT method totals computed on the same geometries. The benchmark is small, well-curated, and chemically diverse — designed to evaluate methods on **relative** energetics: reaction energies (ΔE_rxn), forward barriers (ΔE‡_fwd), and reverse barriers (ΔE‡_rev).
+
+This release packages the structures and reference energies as a single Hugging Face dataset for use evaluating **machine-learning interatomic potentials (MLIPs)** alongside conventional DFT.
+
+> **No forces.** TRIP50 is a single-point energy benchmark on relaxed stationary points. MLIPs that require force training data should look elsewhere; MLIPs being evaluated on relative energetics work directly.
+
+## Contents
+
+| File | Rows | Notes |
+|---|---|---|
+| `data/trip50.extxyz`  | 156 frames | ASE-readable, all 47 method energies on the comment line |
+| `data/species.parquet` | 156 | one row per canonical structure, full float64 precision |
+| `data/reactions.parquet` | 50 | reaction definitions with reference Δs in **kcal/mol** |
+| `data/aliases.parquet` / `aliases.csv` | 30 | logical alias map (no duplicate xyz files shipped) |
+| `data/methods.json` | – | slug ↔ display-name mapping for the 47 method columns |
+| `data/MANIFEST.sha256` | – | digest of every payload file |
+
+## Schemas
+
+### `species.parquet`
+
+| column | type | notes |
+|---|---|---|
+| `species_id` | string | canonical name, e.g. `1-R1`, `26-TS` |
+| `rxn_id` | int32 | 1..50 |
+| `role` | string | `R1` / `R2` / `TS` / `P1` / `P2` |
+| `n_atoms` | int32 | |
+| `atomic_numbers` | list&lt;int8&gt; | length `n_atoms` |
+| `positions` | list&lt;list&lt;float64, 3&gt;&gt; | Cartesian, **Å** |
+| `charge` | int8 | always `0` |
+| `multiplicity` | string | `singlet` / `doublet` / `triplet` |
+| `spin_multiplicity` | int8 | `1` / `2` / `3` |
+| `energy_dlpno_ccsd_t` | float64 | reference, **Hartree** |
+| `energy_<slug>` × 46 | float64 | DFT methods, Hartree (slugs in `methods.json`) |
+
+### `reactions.parquet`
+
+| column | type | notes |
+|---|---|---|
+| `rxn_id` | int32 | 1..50 |
+| `category` | string | one of `C-C`, `C-O`, `C-S`, `HAT`, `Si-X`, `C-Hal`, `N-X` (Figure 2 of the source paper) |
+| `r1_species_id`, `r2_species_id`, `ts_species_id`, `p1_species_id`, `p2_species_id` | string (nullable) | post-alias canonical IDs; `null` = unimolecular leg (contributes 0 H) |
+| `is_unimolecular_reactant`, `is_unimolecular_product` | bool | |
+| `dE_rxn_kcal_dlpno` | float64 | E(P1)+E(P2) − E(R1)−E(R2), **kcal/mol** |
+| `dE_fwd_kcal_dlpno` | float64 | E(TS) − E(R1)−E(R2), kcal/mol |
+| `dE_rev_kcal_dlpno` | float64 | E(TS) − E(P1)−E(P2), kcal/mol |
+
+### `aliases.parquet`
+
+| column | type | notes |
+|---|---|---|
+| `alias` | string | non-canonical species id seen in the rxn definitions |
+| `canonical` | string | the species_id actually present in `species.parquet` |
+
+Aliases are **logical only** — the dataset ships one xyz per canonical structure. A consumer evaluating reaction `50` should look up `p1_species_id = "48-R1"` (which is the same molecule as `50-P1`).
+
+## Conventions
+
+- **Units**. Total energies in `species.parquet` and `trip50.extxyz` are in **Hartree**. Reference reaction-level Δ values in `reactions.parquet` are in **kcal/mol** (1 Hartree = 627.5094740631 kcal/mol).
+- **Charge & spin**. Every species is neutral. Multiplicities span singlet / doublet / triplet. Spin-unaware MLIPs will incur unavoidable error on the radical and triplet species — a known limitation of the dataset for such models.
+- **Unimolecular legs**. Reactions with a single reactant or product have the corresponding `R2` / `P2` field set to `null` in `reactions.parquet`, and the missing partner contributes 0 H to ΔE sums. This convention is inherited from the source paper.
+- **Alias resolution**. Always go through `reactions.parquet` (which already stores post-alias canonical IDs). Never assume `{rxn}-{role}.xyz` exists for every (rxn, role).
+- **Precision**. The Parquet artefacts are full float64. The `trip50.extxyz` representation rounds positions to ASE's default 8-decimal format (≈ 5 × 10⁻⁹ Å — far below physical precision). Use `species.parquet` if exact byte-identity matters.
+
+## Usage
+
+### Hugging Face `datasets`
+
+```python
+from datasets import load_dataset
+
+species = load_dataset("patonlab/trip50", "species", split="train")
+reactions = load_dataset("patonlab/trip50", "reactions", split="train")
+
+print(species[0]["species_id"], species[0]["energy_dlpno_ccsd_t"], "Hartree")
+print(reactions[0]["dE_fwd_kcal_dlpno"], "kcal/mol")
+```
+
+### ASE
+
+```python
+from ase.io import read
+
+frames = read("hf://datasets/patonlab/trip50/data/trip50.extxyz", index=":")
+e_dlpno = frames[0].info["energy_dlpno_ccsd_t"]   # Hartree
+mult = frames[0].info["multiplicity"]              # 'singlet' | 'doublet' | 'triplet'
+```
+
+A reference MLIP evaluator that prints a per-method MAE table is in [`examples/evaluate_mlip.py`](examples/evaluate_mlip.py).
+
+## Limitations
+
+- **No forces or gradients.** Relative reaction energetics only.
+- **Single geometries.** No conformer ensembles; no thermochemical corrections (ΔH / ΔG); no solvent corrections.
+- **Reference is DLPNO-CCSD(T)**, not experiment. Methods that systematically agree with the reference may still disagree with experiment.
+- **Spin-aware models recommended.** Roughly half the species are open-shell.
+
+## Citation
+
+Please cite the original TRIP50 publication when using this dataset. See `CITATION.cff` for a machine-readable record.
+
+```bibtex
+@article{trip50,
+  title   = {Fundamental Study of Density Functional Theory Applied to Triplet State
+             Reactivity: Introduction of the {TRIP50} Data Set},
+  author  = {Hughes, William B. and Popescu, Mihai V. and Paton, Robert S.},
+  journal = {Journal of Chemical Theory and Computation},
+  volume  = {22},
+  pages   = {3530--3542},
+  year    = {2026},
+  doi     = {10.1021/acs.jctc.6c00144}
+}
+```
+
+## License
+
+[Creative Commons Attribution 4.0 International (CC-BY-4.0)](LICENSE).

data/MANIFEST.sha256

@@ -0,0 +1,6 @@
+ca6180cfdfafb26998694109e3d7bb9f712942f82ec5705962dd452e77386982  aliases.csv
+0495a12e261c75e32b912d1a52df5bc851262ea3f9e3ddd1d1ab9097f4bb96ee  aliases.parquet
+88a0fe83e5a6b0367a36c852004d24ac0db7e1aef938956c9d95e19e7453282d  methods.json
+bf5c6e285994b669ef757c542189a6736d06008ffa5494051bd85ff5d264be2d  reactions.parquet
+76c07dc80c72983c0304b290d8efd8465c69a3e50ba1fbad33625357e37b98de  species.parquet
+f90e616177ad8de1f712f49b4ec62afa203db5f9ccc9e49fcb73d18b68a6ab4b  trip50.extxyz

data/aliases.csv

@@ -0,0 +1,32 @@
+alias,canonical
+13-R1,10-R1
+16-R2,15-R2
+17-R2,15-R1
+18-R1,8-R1
+19-R2,18-R2
+2-R1,1-P1
+25-P2,10-P2
+26-P1,25-P1
+26-R1,25-R1
+28-P1,27-P1
+28-P2,10-P2
+28-R1,25-R2
+28-R2,27-R1
+29-P1,27-P1
+29-R2,27-R1
+30-P1,27-P1
+30-R2,27-R1
+39-P2,38-P2
+4-R1,3-R1
+40-P1,38-P1
+41-P1,38-P1
+42-R2,15-R2
+44-R1,43-P1
+46-P1,44-P1
+46-R1,45-P1
+47-P1,44-P1
+48-P1,44-P1
+49-P1,44-P1
+50-P1,48-R1
+6-R1,5-R1
+6-R2,5-R2

data/aliases.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0495a12e261c75e32b912d1a52df5bc851262ea3f9e3ddd1d1ab9097f4bb96ee
+size 2021

data/methods.json

@@ -0,0 +1,286 @@
+{
+  "energy_columns": [
+    {
+      "slug": "energy_dlpno_ccsd_t",
+      "display_name": "DLPNO-CCSD(T)",
+      "is_reference": true,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_b2gp_plyp_d4",
+      "display_name": "B2GP-PLYP-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_b2plyp_d4",
+      "display_name": "B2PLYP-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_b3lyp_d4",
+      "display_name": "B3LYP-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_b3pw91_d4",
+      "display_name": "B3PW91-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_b97_d3_op",
+      "display_name": "B97-D3(op)",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_b97m_v",
+      "display_name": "B97M-V",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_blyp_d4",
+      "display_name": "BLYP-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_cam_b3lyp_d4",
+      "display_name": "CAM-B3LYP-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_hf_d4",
+      "display_name": "HF-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_kappapr2scan50_d4",
+      "display_name": "\u03baPr2SCAN50-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_lc_omegapbe08_d3_bj",
+      "display_name": "LC-\u03c9PBE08-D3(BJ)",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_lrc_omegapbe_d3_bj",
+      "display_name": "LRC-\u03c9PBE-D3(BJ)",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_lrc_omegapbeh_d3",
+      "display_name": "LRC-\u03c9PBEh-D3",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_m05_2x_d3",
+      "display_name": "M05-2X-D3",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_m06_d4",
+      "display_name": "M06-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_m06_2x_d3",
+      "display_name": "M06-2X-D3",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_m06_hf_d3",
+      "display_name": "M06-HF-D3",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_m06_l_d4",
+      "display_name": "M06-L-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_mn15_d3",
+      "display_name": "MN15-D3",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_mpw2plyp_d4",
+      "display_name": "mPW2PLYP-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_o3lyp_d4",
+      "display_name": "O3LYP-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_olyp_d3_bj",
+      "display_name": "OLYP-D3(BJ)",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_pbe_d4",
+      "display_name": "PBE-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_pbe0_d3_op",
+      "display_name": "PBE0-D3(op)",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_pbe0_dh_d3_bj",
+      "display_name": "PBE0-DH-D3(BJ)",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_pbe_qidh_d3_bj",
+      "display_name": "PBE-QIDH-D3(BJ)",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_pr2scan50_d4",
+      "display_name": "Pr2SCAN50-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_pr2scan69_d4",
+      "display_name": "Pr2SCAN69-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_pb6b95_d3_bj",
+      "display_name": "PB6B95-D3(BJ)",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_pw91_d4",
+      "display_name": "PW91-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_pwpb95_d4",
+      "display_name": "PWPB95-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_r2scan_d4",
+      "display_name": "r2SCAN-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_r2scan0_d4",
+      "display_name": "r2SCAN0-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_r2scan50_d4",
+      "display_name": "r2SCAN50-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_r2scanh_d4",
+      "display_name": "r2SCANh-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_rev_dsd_pbep86_d4",
+      "display_name": "rev-DSD-PBEP86-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_tpss_d4",
+      "display_name": "TPSS-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_tpssh_d4",
+      "display_name": "TPSSh-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_omegab97m_2_v",
+      "display_name": "\u03c9B97M(2)-V",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_omegab97m_v",
+      "display_name": "\u03c9B97M-V",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_omegab97x_d4",
+      "display_name": "\u03c9B97X-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_omegab97x_2_d3_bj",
+      "display_name": "\u03c9B97X-2-D3(BJ)",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_omegab97x_v",
+      "display_name": "\u03c9B97X-V",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_omegam06_d3",
+      "display_name": "\u03c9M06-D3",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_omegar2scan_d4",
+      "display_name": "\u03c9r2SCAN-D4",
+      "is_reference": false,
+      "units": "hartree"
+    },
+    {
+      "slug": "energy_x3lyp_d4",
+      "display_name": "X3LYP-D4",
+      "is_reference": false,
+      "units": "hartree"
+    }
+  ]
+}

data/reactions.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf5c6e285994b669ef757c542189a6736d06008ffa5494051bd85ff5d264be2d
+size 10594

data/species.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76c07dc80c72983c0304b290d8efd8465c69a3e50ba1fbad33625357e37b98de
+size 190123

examples/evaluate_mlip.py

@@ -0,0 +1,93 @@
+"""Reference MLIP evaluator for TRIP50.
+
+Loads structures from `data/trip50.extxyz`, runs an ASE Calculator over every
+frame, then computes per-reaction ΔE_rxn / ΔE_fwd / ΔE_rev (kcal/mol) using the
+reaction definitions in `data/reactions.parquet`. Reports an MAE table against
+the DLPNO-CCSD(T) reference.
+
+Replace `make_calculator()` below with your MLIP. The placeholder uses
+`ase.calculators.emt.EMT` so the script runs CPU-only out of the box; EMT
+support spans only a small subset of the periodic table, so most species will
+fall back to NaN and the MAE table will look terrible — that is expected. The
+purpose of this file is to show the wiring.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from ase.calculators.calculator import Calculator
+from ase.calculators.emt import EMT
+from ase.io import read
+
+EV_TO_HARTREE = 1.0 / 27.211386245988
+HARTREE_TO_KCAL = 627.5094740631
+
+DATA = Path(__file__).resolve().parent.parent / "data"
+
+
+def make_calculator() -> Calculator:
+    """Replace with your MLIP's ASE Calculator."""
+    return EMT()
+
+
+def species_total_energies(extxyz_path: Path, calc: Calculator) -> dict[str, float]:
+    """Run `calc` over every frame; return {species_id: total_energy in Hartree}."""
+    totals: dict[str, float] = {}
+    for atoms in read(extxyz_path, index=":"):
+        atoms.calc = calc
+        try:
+            e_ev = atoms.get_potential_energy()
+            totals[atoms.info["species_id"]] = e_ev * EV_TO_HARTREE
+        except Exception as exc:
+            print(f"  {atoms.info['species_id']}: {type(exc).__name__}: {exc}")
+            totals[atoms.info["species_id"]] = float("nan")
+    return totals
+
+
+def reaction_predictions(reactions: pd.DataFrame, totals: dict[str, float]) -> pd.DataFrame:
+    """Predict ΔE_rxn, ΔE_fwd, ΔE_rev (kcal/mol) per reaction. Missing partners
+    (unimolecular legs) contribute 0."""
+    def E(sid):
+        return 0.0 if sid is None else totals.get(sid, float("nan"))
+
+    out = reactions.copy()
+    R = out.r1_species_id.map(E) + out.r2_species_id.map(E)
+    P = out.p1_species_id.map(E) + out.p2_species_id.map(E)
+    TS = out.ts_species_id.map(E)
+    out["dE_rxn_kcal_pred"] = (P - R) * HARTREE_TO_KCAL
+    out["dE_fwd_kcal_pred"] = (TS - R) * HARTREE_TO_KCAL
+    out["dE_rev_kcal_pred"] = (TS - P) * HARTREE_TO_KCAL
+    return out
+
+
+def mae(a: pd.Series, b: pd.Series) -> float:
+    diff = (a - b).dropna()
+    return float(diff.abs().mean())
+
+
+def main() -> None:
+    extxyz = DATA / "trip50.extxyz"
+    reactions = pd.read_parquet(DATA / "reactions.parquet")
+
+    print(f"Loading {extxyz}")
+    calc = make_calculator()
+
+    print(f"Running {type(calc).__name__} over 156 frames…")
+    totals = species_total_energies(extxyz, calc)
+    n_ok = sum(1 for v in totals.values() if not np.isnan(v))
+    print(f"  ok: {n_ok}/{len(totals)}")
+
+    pred = reaction_predictions(reactions, totals)
+    print(
+        f"\nMAE vs DLPNO-CCSD(T) (kcal/mol):\n"
+        f"  ΔE_rxn = {mae(pred.dE_rxn_kcal_pred, pred.dE_rxn_kcal_dlpno):.2f}\n"
+        f"  ΔE_fwd = {mae(pred.dE_fwd_kcal_pred, pred.dE_fwd_kcal_dlpno):.2f}\n"
+        f"  ΔE_rev = {mae(pred.dE_rev_kcal_pred, pred.dE_rev_kcal_dlpno):.2f}"
+    )
+
+
+if __name__ == "__main__":
+    main()

examples/verify_release.py

@@ -0,0 +1,137 @@
+"""Round-trip verification of the TRIP50 release artefacts.
+
+Three checks, run in order:
+
+  1.  MANIFEST.sha256 matches every payload file's actual digest.
+  2.  Recomputed per-method MAE table from species.parquet reproduces the
+      published trip50_dft_mae.csv to within 0.005 kcal/mol per cell.
+  3.  Extended-XYZ round-trip: every frame's species_id, atomic_numbers, and
+      positions agree with species.parquet to within 1e-7 Å (extxyz default
+      8-digit precision).
+
+Run from the release root or the examples/ directory.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from ase.io import read
+
+ROOT = Path(__file__).resolve().parent.parent
+DATA = ROOT / "data"
+PRODUCTION = Path("/Users/rpaton/TRIP50/production")
+
+sys.path.insert(0, str(ROOT / "scripts"))
+from trip50_core import reaction_quantities  # noqa: E402
+
+POSITION_TOL = 1e-7
+MAE_TOL = 0.005  # kcal/mol; published table has 3-dp rounding
+
+
+def check_manifest() -> int:
+    manifest = DATA / "MANIFEST.sha256"
+    failed = 0
+    for line in manifest.read_text().strip().splitlines():
+        digest, name = line.split("  ", 1)
+        actual = hashlib.sha256((DATA / name).read_bytes()).hexdigest()
+        ok = actual == digest
+        print(f"  {'ok ' if ok else 'BAD'} {name}")
+        if not ok:
+            failed += 1
+    return failed
+
+
+def _build_series(species: pd.DataFrame, slug_to_canonical: dict[str, str]) -> dict[str, pd.Series]:
+    """For every method slug, return a Series indexed by species_id (post-alias
+    canonical names) with energies in Hartree."""
+    out = {}
+    for slug, _display in slug_to_canonical.items():
+        out[slug] = species.set_index("species_id")[slug]
+    return out
+
+
+def check_mae_table() -> int:
+    species = pd.read_parquet(DATA / "species.parquet")
+    methods = pd.read_json(DATA / "methods.json")["energy_columns"]
+    slug_to_display = {m["slug"]: m["display_name"] for m in methods}
+    aliases_df = pd.read_parquet(DATA / "aliases.parquet")
+    alias_map = dict(zip(aliases_df["alias"], aliases_df["canonical"]))
+
+    sp_indexed = species.set_index("species_id")
+    dlpno = sp_indexed["energy_dlpno_ccsd_t"]
+    ref_rxn, ref_fwd, ref_rev = reaction_quantities(dlpno, dlpno, alias_map)
+
+    pub_path = PRODUCTION / "trip50_dft_mae.csv"
+    if not pub_path.exists():
+        print(f"  SKIP: {pub_path} not present (offline verification only)")
+        return 0
+    pub = pd.read_csv(pub_path).set_index("Theory")
+
+    failed = 0
+    for slug, display in slug_to_display.items():
+        if display == "DLPNO-CCSD(T)" or display not in pub.index:
+            continue
+        col = sp_indexed[slug]
+        t_rxn, t_fwd, t_rev = reaction_quantities(col, dlpno, alias_map)
+        keys = sorted(set(ref_rxn) & set(t_rxn))
+        mae_rxn = sum(abs(ref_rxn[k] - t_rxn[k]) for k in keys) / len(keys)
+        mae_fwd = sum(abs(ref_fwd[k] - t_fwd[k]) for k in keys) / len(keys)
+        mae_rev = sum(abs(ref_rev[k] - t_rev[k]) for k in keys) / len(keys)
+        diffs = [
+            abs(round(mae_rxn, 3) - pub.loc[display, "ΔE_rxn (P-R)"]),
+            abs(round(mae_fwd, 3) - pub.loc[display, "ΔE_fwd (TS-R)"]),
+            abs(round(mae_rev, 3) - pub.loc[display, "ΔE_rev (TS-P)"]),
+        ]
+        ok = max(diffs) < MAE_TOL
+        if not ok:
+            failed += 1
+            print(f"  BAD {display}: max abs diff = {max(diffs):.4f} kcal/mol")
+    print(f"  {'ok ' if failed == 0 else 'BAD'} MAE table reproduces "
+          f"trip50_dft_mae.csv (max tol {MAE_TOL})")
+    return failed
+
+
+def check_extxyz_roundtrip() -> int:
+    frames = read(DATA / "trip50.extxyz", index=":")
+    species = pd.read_parquet(DATA / "species.parquet").set_index("species_id")
+    failed = 0
+    for atoms in frames:
+        sid = atoms.info.get("species_id")
+        if sid is None or sid not in species.index:
+            print(f"  BAD frame missing/unknown species_id: {sid!r}")
+            failed += 1
+            continue
+        row = species.loc[sid]
+        if atoms.numbers.tolist() != list(row["atomic_numbers"]):
+            print(f"  BAD {sid}: atomic_numbers mismatch")
+            failed += 1
+            continue
+        pos = np.asarray(row["positions"].tolist())
+        max_dev = float(np.abs(atoms.positions - pos).max())
+        if max_dev > POSITION_TOL:
+            print(f"  BAD {sid}: positions max dev {max_dev:.2e} Å > {POSITION_TOL}")
+            failed += 1
+    print(f"  {'ok ' if failed == 0 else 'BAD'} extxyz round-trip "
+          f"({len(frames)} frames, tol {POSITION_TOL} Å)")
+    return failed
+
+
+def main() -> int:
+    print("MANIFEST")
+    f1 = check_manifest()
+    print("\nMAE table")
+    f2 = check_mae_table()
+    print("\nExtxyz round-trip")
+    f3 = check_extxyz_roundtrip()
+    total = f1 + f2 + f3
+    print(f"\n{'PASS' if total == 0 else 'FAIL'} ({total} failure(s))")
+    return total
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/build_hf_release.py

@@ -0,0 +1,262 @@
+"""Build the TRIP50 Hugging Face release artefacts under ../data/.
+
+Reads from /Users/rpaton/TRIP50/production/:
+  - trip50_reference_energies.csv   (DLPNO + 46 DFT totals, Hartree)
+  - trip50_species_aliases.csv      (alias → canonical map)
+  - xyz_corrected/*.xyz             (156 standard XYZ files; comment line = multiplicity)
+
+Writes to ../data/:
+  - trip50.extxyz        : 156 frames, ASE-readable, all energies in info dict
+  - species.parquet      : per-species table with atomic_numbers/positions/energies
+  - reactions.parquet    : 50 rxn rows, alias-resolved species ids + reference Δs (kcal/mol)
+  - aliases.parquet      : alias map mirrored as parquet (CSV is also copied)
+  - aliases.csv          : verbatim copy of source alias table
+  - methods.json         : slug ↔ display-name mapping for the 46 DFT columns
+  - MANIFEST.sha256      : SHA-256 of every payload file (deterministic check)
+
+The build is deterministic: sorted iteration, no timestamps in any payload.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import shutil
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+from ase import Atoms
+from ase.io import write as ase_write
+
+from trip50_core import (
+    HARTREE_TO_KCAL,
+    ROLES,
+    lookup,
+    reaction_quantities,
+    slugify_method,
+)
+
+SRC = Path("/Users/rpaton/TRIP50/production")
+OUT = Path(__file__).resolve().parent.parent / "data"
+XYZ_DIR = SRC / "xyz_corrected"
+
+REF_CSV = SRC / "trip50_reference_energies.csv"
+ALIAS_CSV = SRC / "trip50_species_aliases.csv"
+
+MULT_TO_INT = {"singlet": 1, "doublet": 2, "triplet": 3}
+
+# Reaction categories from Figure 2 of Hughes, Popescu, Paton, JCTC 2026, 22, 3530.
+RXN_CATEGORIES = (
+    ("C-C",   range(1, 13)),
+    ("C-O",   range(13, 18)),
+    ("C-S",   range(18, 22)),
+    ("HAT",   range(22, 31)),
+    ("Si-X",  range(31, 38)),
+    ("C-Hal", range(38, 42)),
+    ("N-X",   range(42, 51)),
+)
+RXN_CATEGORY = {r: name for name, rng in RXN_CATEGORIES for r in rng}
+assert set(RXN_CATEGORY) == set(range(1, 51)), "category ranges must tile 1..50 exactly"
+
+
+def parse_xyz(path: Path) -> tuple[Atoms, str]:
+    """Parse a TRIP50 standard-XYZ file. Returns (Atoms, multiplicity_label)."""
+    lines = path.read_text().splitlines()
+    n = int(lines[0].strip())
+    mult = lines[1].strip()
+    symbols, coords = [], []
+    for line in lines[2:2 + n]:
+        parts = line.split()
+        symbols.append(parts[0])
+        coords.append([float(x) for x in parts[1:4]])
+    atoms = Atoms(symbols=symbols, positions=np.asarray(coords, dtype=np.float64))
+    return atoms, mult
+
+
+def species_id_from_filename(path: Path) -> str:
+    return path.stem  # e.g. "1-R1.xyz" → "1-R1"
+
+
+def split_rxn_role(species_id: str) -> tuple[int, str]:
+    rxn_str, role = species_id.split("-", 1)
+    return int(rxn_str), role
+
+
+def build():
+    OUT.mkdir(parents=True, exist_ok=True)
+
+    # -------------------- inputs --------------------
+    ref = pd.read_csv(REF_CSV).set_index("Species")
+    aliases_df = pd.read_csv(ALIAS_CSV)
+    alias_map = dict(zip(aliases_df["alias"], aliases_df["canonical"]))
+    dlpno_col = "DLPNO-CCSD(T)"
+
+    # Method display names → slugs (DLPNO + 46 DFT)
+    method_columns = list(ref.columns)
+    slug_map = {col: ("energy_dlpno_ccsd_t" if col == dlpno_col
+                      else f"energy_{slugify_method(col)}")
+                for col in method_columns}
+    # Verify uniqueness
+    if len(set(slug_map.values())) != len(slug_map):
+        dupes = [s for s in slug_map.values() if list(slug_map.values()).count(s) > 1]
+        raise RuntimeError(f"Slug collision: {set(dupes)}")
+
+    methods_meta = {
+        "energy_columns": [
+            {"slug": slug_map[col], "display_name": col,
+             "is_reference": col == dlpno_col,
+             "units": "hartree"}
+            for col in method_columns
+        ],
+    }
+    (OUT / "methods.json").write_text(json.dumps(methods_meta, indent=2) + "\n")
+
+    # -------------------- structures + per-species rows --------------------
+    xyz_paths = sorted(XYZ_DIR.glob("*.xyz"),
+                       key=lambda p: split_rxn_role(p.stem))
+
+    species_rows = []
+    frames: list[Atoms] = []
+
+    for xyz_path in xyz_paths:
+        species_id = species_id_from_filename(xyz_path)
+        atoms, mult_label = parse_xyz(xyz_path)
+        rxn_id, role = split_rxn_role(species_id)
+
+        if mult_label not in MULT_TO_INT:
+            raise ValueError(f"Unknown multiplicity {mult_label!r} in {xyz_path}")
+        spin_mult = MULT_TO_INT[mult_label]
+
+        # Pull every method's total energy for this species (alias-resolved)
+        per_method = {}
+        for col in method_columns:
+            val = lookup(species_id, ref[col], alias_map)
+            if val is None:
+                raise RuntimeError(f"Missing {col} energy for {species_id}")
+            per_method[slug_map[col]] = float(val)
+
+        # Frame info dict — ASE will serialise this onto the comment line
+        info = {
+            "species_id": species_id,
+            "rxn": rxn_id,
+            "role": role,
+            "multiplicity": mult_label,
+            "spin_multiplicity": spin_mult,
+            "charge": 0,
+            "units_energy": "hartree",
+            **per_method,
+        }
+        atoms.info.update(info)
+        frames.append(atoms)
+
+        species_rows.append({
+            "species_id": species_id,
+            "rxn_id": rxn_id,
+            "role": role,
+            "n_atoms": len(atoms),
+            "atomic_numbers": atoms.numbers.tolist(),
+            "positions": atoms.positions.tolist(),
+            "charge": 0,
+            "multiplicity": mult_label,
+            "spin_multiplicity": spin_mult,
+            **per_method,
+        })
+
+    # -------------------- write extxyz --------------------
+    extxyz_path = OUT / "trip50.extxyz"
+    ase_write(extxyz_path, frames, format="extxyz")
+
+    # -------------------- write species.parquet --------------------
+    species_df = pd.DataFrame(species_rows)
+    species_schema = pa.schema([
+        ("species_id", pa.string()),
+        ("rxn_id", pa.int32()),
+        ("role", pa.string()),
+        ("n_atoms", pa.int32()),
+        ("atomic_numbers", pa.list_(pa.int8())),
+        ("positions", pa.list_(pa.list_(pa.float64(), 3))),
+        ("charge", pa.int8()),
+        ("multiplicity", pa.string()),
+        ("spin_multiplicity", pa.int8()),
+        *[(slug_map[col], pa.float64()) for col in method_columns],
+    ])
+    species_table = pa.Table.from_pandas(species_df, schema=species_schema, preserve_index=False)
+    pq.write_table(species_table, OUT / "species.parquet", compression="zstd")
+
+    # -------------------- reactions.parquet --------------------
+    dlpno_series = ref[dlpno_col]
+    dE_rxn, dE_fwd, dE_rev = reaction_quantities(dlpno_series, dlpno_series, alias_map)
+
+    reaction_rows = []
+    for r in range(1, 51):
+        present = {role: lookup(f"{r}-{role}", dlpno_series, alias_map) is not None
+                   for role in ROLES}
+
+        def canonical(role):
+            sid = f"{r}-{role}"
+            return alias_map.get(sid, sid) if present[role] else None
+
+        reaction_rows.append({
+            "rxn_id": r,
+            "category": RXN_CATEGORY[r],
+            "r1_species_id": canonical("R1"),
+            "r2_species_id": canonical("R2"),
+            "ts_species_id": canonical("TS"),
+            "p1_species_id": canonical("P1"),
+            "p2_species_id": canonical("P2"),
+            "is_unimolecular_reactant": not present["R2"],
+            "is_unimolecular_product": not present["P2"],
+            "dE_rxn_kcal_dlpno": dE_rxn.get(r),
+            "dE_fwd_kcal_dlpno": dE_fwd.get(r),
+            "dE_rev_kcal_dlpno": dE_rev.get(r),
+        })
+
+    reactions_df = pd.DataFrame(reaction_rows)
+    reactions_schema = pa.schema([
+        ("rxn_id", pa.int32()),
+        ("category", pa.string()),
+        ("r1_species_id", pa.string()),
+        ("r2_species_id", pa.string()),
+        ("ts_species_id", pa.string()),
+        ("p1_species_id", pa.string()),
+        ("p2_species_id", pa.string()),
+        ("is_unimolecular_reactant", pa.bool_()),
+        ("is_unimolecular_product", pa.bool_()),
+        ("dE_rxn_kcal_dlpno", pa.float64()),
+        ("dE_fwd_kcal_dlpno", pa.float64()),
+        ("dE_rev_kcal_dlpno", pa.float64()),
+    ])
+    reactions_table = pa.Table.from_pandas(reactions_df, schema=reactions_schema, preserve_index=False)
+    pq.write_table(reactions_table, OUT / "reactions.parquet", compression="zstd")
+
+    # -------------------- aliases --------------------
+    shutil.copyfile(ALIAS_CSV, OUT / "aliases.csv")
+    aliases_table = pa.Table.from_pandas(aliases_df, preserve_index=False)
+    pq.write_table(aliases_table, OUT / "aliases.parquet", compression="zstd")
+
+    # -------------------- MANIFEST --------------------
+    payload = sorted([
+        OUT / "trip50.extxyz",
+        OUT / "species.parquet",
+        OUT / "reactions.parquet",
+        OUT / "aliases.parquet",
+        OUT / "aliases.csv",
+        OUT / "methods.json",
+    ])
+    manifest_lines = []
+    for path in payload:
+        digest = hashlib.sha256(path.read_bytes()).hexdigest()
+        manifest_lines.append(f"{digest}  {path.name}")
+    (OUT / "MANIFEST.sha256").write_text("\n".join(manifest_lines) + "\n")
+
+    print(f"Wrote {len(species_df)} species frames, {len(reactions_df)} reactions to {OUT}")
+    for path in payload + [OUT / "MANIFEST.sha256"]:
+        size = path.stat().st_size
+        print(f"  {path.name:30s} {size:>10,} B")
+
+
+if __name__ == "__main__":
+    build()

scripts/trip50_core.py

@@ -0,0 +1,69 @@
+"""Core helpers for the TRIP50 dataset: alias resolution and reaction quantities.
+
+Used by the release producer (`build_hf_release.py`), the round-trip verifier
+(`examples/verify_release.py`), and the example MLIP evaluator
+(`examples/evaluate_mlip.py`). Kept dependency-light so it ships with the
+release without pulling in optional libraries.
+"""
+
+from __future__ import annotations
+
+HARTREE_TO_KCAL = 627.5094740631
+ROLES = ("R1", "R2", "TS", "P1", "P2")
+
+
+def lookup(species_name, source, alias_map):
+    """Energy for `species_name` in `source` (a Mapping or pandas Series indexed
+    by species id), with alias resolution. Returns None if not present."""
+    name = alias_map.get(species_name, species_name)
+    return source.get(name, None)
+
+
+def required_roles(rxn_id, dlpno_source, alias_map):
+    """Roles that the rxn definition requires (i.e., DLPNO has a non-null value
+    for that species). Unimolecular legs (R2 and/or P2 absent) drop out."""
+    return {role for role in ROLES
+            if lookup(f"{rxn_id}-{role}", dlpno_source, alias_map) is not None}
+
+
+def reaction_quantities(source, dlpno_source, alias_map):
+    """Compute ΔE_rxn, ΔE_fwd, ΔE_rev (kcal/mol) for every TRIP50 reaction
+    defined in `dlpno_source`. A reaction's quantity is included only when
+    every role required by the DLPNO definition is also resolvable in `source`.
+    Unimolecular legs (roles absent from DLPNO) contribute 0 H on both sides.
+
+    Returns three dicts {rxn_id: kcal_value}.
+    """
+    rxn, fwd, rev = {}, {}, {}
+    for r in range(1, 51):
+        req = required_roles(r, dlpno_source, alias_map)
+        e = {role: (lookup(f"{r}-{role}", source, alias_map) or 0.0) for role in ROLES}
+        ok = lambda needed: all((lookup(f"{r}-{role}", source, alias_map) is not None)
+                                for role in (needed & req))
+        if {"R1", "P1"} <= req and ok({"R1", "R2", "P1", "P2"}):
+            rxn[r] = ((e["P1"] + e["P2"]) - (e["R1"] + e["R2"])) * HARTREE_TO_KCAL
+        if {"R1", "TS"} <= req and ok({"R1", "R2", "TS"}):
+            fwd[r] = (e["TS"] - (e["R1"] + e["R2"])) * HARTREE_TO_KCAL
+        if {"P1", "TS"} <= req and ok({"P1", "P2", "TS"}):
+            rev[r] = (e["TS"] - (e["P1"] + e["P2"])) * HARTREE_TO_KCAL
+    return rxn, fwd, rev
+
+
+def slugify_method(name):
+    """Reversible slug for a method/theory name. Mapping is recorded in
+    methods.json at release time so consumers can recover the display name."""
+    s = name
+    s = s.replace("ω", "omega").replace("Ω", "omega")
+    s = s.replace("κ", "kappa").replace("Κ", "kappa")
+    s = s.lower()
+    out = []
+    for ch in s:
+        if ch.isalnum():
+            out.append(ch)
+        elif ch in "-/() ":
+            out.append("_")
+        # drop everything else
+    slug = "".join(out)
+    while "__" in slug:
+        slug = slug.replace("__", "_")
+    return slug.strip("_")