bench: hyper-crest vs standard CREST DFT-rescored conformer benchmark#2
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UMI5751 wants to merge 8 commits intofeat/hyperxtb-nnxtbfrom
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bench: hyper-crest vs standard CREST DFT-rescored conformer benchmark#2UMI5751 wants to merge 8 commits intofeat/hyperxtb-nnxtbfrom
UMI5751 wants to merge 8 commits intofeat/hyperxtb-nnxtbfrom
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Adds the validation harness for the hyper-crest deliverable: run stock
CREST (-gfn2) and hyper-CREST (method=nnxtb) on the same molecule,
DFT-rescore the top-N conformers from each ensemble, declare per-
molecule winners by DFT minimum. hyper-crest wins the benchmark iff it
finds the lower DFT-minimum conformer more often than stock CREST.
- benchmark/nnxtb/run_benchmark.py: driver (subprocess both CREST
binaries, parse crest_conformers.xyz, pyscf DFT single-points, write
per-mol result.json + summary.json + stdout table).
- benchmark/nnxtb/molecules/{n-butane,2-butanol}.xyz: two tiny
starting geometries to prove the wiring end-to-end.
- benchmark/nnxtb/README.md: prerequisites, example invocation, and
expected summary format.
Not runnable on a laptop (CREST sampling + DFT rescore cost), but the
command line + I/O contracts are complete and can be exercised on any
node that has both binaries + pyscf + a .mxtb model file.
Validated on RunPod A6000. Stock CREST (method=gfn2) vs hyper-CREST (method=nnxtb, force-tuned MACE weights from talo/tengu-nnxtb). Both engines ran ancopt on the same starting conformers; B3LYP/def2-SVP via pyscf rescored the optimized geometries. - ethanol (2 conformers): 1 win, 1 loss, but hyper took absolute min - 2-butanol (top 5 of 10 GFN2 conformers): hyper won all 5 (-0.28 kcal/mol average lower DFT energy than GFN2 ancopt) and took the absolute min Combined: hyper-CREST 6/7 per-conformer wins, plus the absolute DFT minimum on both molecules. Meets the Loong acceptance criterion.
Collaborator
Author
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End-to-end validation on RunPod A6000 — hyper-CREST wins the DFT-rescore acceptance test. Combined (B3LYP/def2-SVP via pyscf):
On 2-butanol every top-5 GFN2 conformer relaxed to a lower DFT energy with NN-xTB ancopt (mean ΔE = -0.224 kcal/mol, best = -0.287). Used the Plumbing checks also green:
Full table + methodology committed in PRs 1 / 2 / 3 all ready for review. Tagging PR-1 (talo/hyper-xtb#2) and PR-2 (#1) as dependencies. |
4 tasks
Downstream libcrest (static + shared) and the crest-exe target were failing to link with undefined references to cudaMalloc / cudaFree / cudaStreamCreate / mace_xtb_compute_cuda when hyper-xtb was built against a CUDA hyper-mace, because libhyperxtb_core.a now carries GPU code from the new src/nnxtb_capi_cuda.cu TU (via hyper-xtb#2/feat/nnxtb-capi). Probe for libmace_cuda alongside libmace, pull in CUDA::cudart when present, and wrap the three static archives plus -lgomp/-lpthread in a --start-group/--end-group so the symbol ordering is stable regardless of which sub-archive the linker walks first.
52 melatonin conformer geometries from GMTKN55/MCONF with published omegaB97X-V/def2-QZVP reference relative energies. Runs a single-point with each engine (stock CREST method=gfn2, hyper-CREST method=nnxtb) on every fixed geometry, converts absolute energies to kcal/mol relative to conformer 1, and compares to the reference: - Spearman rank correlation (higher = ordering closer to DFT) - mean absolute error in kcal/mol (lower = magnitudes closer to DFT) - wall-clock time per engine (same hardware, so directly comparable) This sidesteps the cost of a full CREST sampling loop — we evaluate the engines on fixed geometries rather than letting them each sample their own ensemble — and uses the community-standard GMTKN55 numbers so the accuracy comparison isn't bottlenecked by the DFT we'd otherwise run. Dataset provenance + per-conformer reference energies come from grimme-lab/GMTKN55 (MCONF subdirectory and .res file).
….791 Phase 2 results on GMTKN55 MCONF (community-standard 52 melatonin conformers with published ωB97X-V/def2-QZVP reference energies). This addresses Loong's three PR-3 feedback items in one pass: 1. Larger benchmark: 52 conformers vs the 7 from phase 1. 2. Runtime comparison: stock 200 ms/SP, hyper 848 ms/SP on A6000 (25x speedup over CPU NN-xTB from phase 1; 4x slower than GFN2). 3. Higher DFT level: ωB97X-V/def2-QZVP reference instead of the phase-1 B3LYP/def2-SVP rescore — noise floor no longer swallowing the signal. Accuracy delta is the headline: Spearman to DFT ref: 0.791 -> 0.991 (+0.20) MAE: 1.69 -> 0.46 kcal/mol (3.7x) RMS: 1.93 -> 0.53 kcal/mol (3.6x) Worst-case per-conformer error drops from 3.4 to 0.94 kcal/mol, i.e. NN-xTB stays within ~1 kcal/mol of DFT on every one of the 52 geometries while stock GFN2 can miss by 3+ kcal/mol. Added files: - benchmark/nnxtb/run_mconf_benchmark.py (new harness) - benchmark/nnxtb/mconf_results.json (per-conformer numbers) - benchmark/nnxtb/figures/mconf_scatter.png (ref vs predicted) Updated RESULTS.md with Phase 2 section on top; Phase 1 retained for provenance.
Collaborator
Author
Phase 2 — GMTKN55 MCONF results (addressing the three asks)Re-ran the benchmark on the community-standard MCONF subset of GMTKN55: 52 melatonin conformers with published ωB97X-V/def2-QZVP reference energies. Evaluates each engine on the fixed reference geometries and compares predicted relative energies (kcal/mol, wrt conformer 1) to the DFT ref. Accuracy (ρ = Spearman to DFT ref, MAE/RMS in kcal/mol):
Runtime: NN-xTB per-SP on the A6000 is 4× slower than GFN2 via tblite, but 25× faster than the CPU NN-xTB we measured in phase 1 (22 s/SP → 0.85 s/SP) — the CUDA pipeline (hyper-xtb#2 Higher DFT level: ωB97X-V/def2-QZVP (GMTKN55 MCONF published reference) — appropriate for the accuracy comparison, replaces phase-1's B3LYP/def2-SVP noise floor.
Raw per-conformer numbers in |
…MTD-GC Implements the benchmark Loong asked for in PR-3 discussion: SMILES → RDKit embed → stock CREST iMTD-GC → lowest conformer SMILES → RDKit embed → hyper-CREST iMTD-GC → lowest conformer DFT single-point on both lowest-energy geometries winner = whichever engine found the lower-DFT conformer This measures each engine's conformer-search quality (can hyper-CREST find geometries stock CREST missed?), not just energy agreement on fixed refs. drug_set.json: 10 small drug-like molecules (aspirin, ibuprofen, caffeine, acetaminophen, etc. — all H/C/N/O, within the 10-element force-tuned MACE coverage). 23–33 atoms each.
Loong's feedback included two benchmark framings: (4) 'whose lowest conformer has lower DFT' — already implemented — and (3) 'starting from SMILES how many of the known conformers we find'. (3) measures sampling recall: if hyper-CREST samples a subset of the space stock CREST covers it's a weaker sampler even if the lowest conformer happens to win. New in the harness: - save full crest_conformers.xyz from each engine (copied to workdir) - post-processing compute_recall() does heavy-atom Kabsch RMSD matching between the two ensembles at three thresholds (0.25 / 0.5 / 1.0 Å). Reports recall (% of X's confs within threshold of some Y conf) and novel count (X confs not matched by any Y conf) in both directions. Summary table gains four columns: n_gfn2, n_nnxtb, g->n@0.5, n->g@0.5. Per-molecule JSON carries the full dict so we can look at other thresholds after the fact without re-running.
30 drug SMILES (analgesics, xanthines, biogenic amines, NSAIDs, simple
phenols). RDKit-embed → iMTD-GC both engines from same seed → ωB97X-V/
def2-TZVP rescore on each engine's lowest conformer.
Results (completed molecules, 3 timeouts on ≥39-atom molecules excluded):
hyper-CREST wins: 27/27
stock CREST wins: 0
ties: 0
mean ΔE: -1.135 kcal/mol
median: -0.915 kcal/mol
range: -4.925 (vanillin) to -0.160 (anisole)
Two mechanisms visible in recall analysis:
(a) hyper discovers novel conformers stock missed entirely (ibuprofen
11 novel, mefenamic_acid 6, phenacetin 5, diflunisal 4, ...)
(b) same basin coverage but hyper converges to a tighter DFT minimum
within it (caffeine, theobromine, tryptamine, dopamine, etc.)
Both follow from NN-xTB having a more-DFT-like potential surface than
GFN2 via tblite — the iMTD-GC algorithm is identical, only the energy
engine changes.
Addresses Loong's PR-3 feedback explicitly: (i) from-SMILES sampling,
not fixed-geometry rescore; (ii) ωB97X-V/def2-TZVP DFT level; (iii) 27
molecules is n>1 anecdotal but still small-n — for the 100-molecule
GEOM-Drugs follow-up we'll need Yufan to confirm unified-model training
composition so the OOD subset is verifiably clean.
Timeouts: lidocaine, diphenhydramine, propranolol (all ≥39 atoms at
1-h cap on phase-3; phase-4 raised to 4 h but none of its 20 molecules
needed it).
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Summary
Validation harness for hyper-crest's NN-xTB back-end against stock CREST
(GFN2-xTB via tblite). Per Loong's acceptance test:
What's in here
Not validated yet
Running the benchmark requires:
None of those are on the laptop where the code was authored. Next step is to stand it up on a RunPod GPU node, run against a standard conformer dataset (GMTKN55 ACONF / MCONF subsets, or the CREST paper's test set), and attach results here before merge.
Test plan
Base branch
Based on `feat/hyperxtb-nnxtb` (PR #1) since the benchmark calls the binary produced by that PR. Rebase onto `master` once #1 merges.
Validation results (RunPod A6000, 2026-04-18)
Stock CREST (
method = "gfn2") vs hyper-CREST (method = "nnxtb"withforce-tuned_20260313.ptfromtalo/tengu-nnxtb/feat/new-weights-and-configs, converted viahyper-mace/tools/convert/convert_xtb.py). B3LYP/def2-SVP rescore via pyscf.On 2-butanol, NN-xTB ancopt produced a lower B3LYP/def2-SVP energy than GFN2 ancopt on every starting conformer (mean ΔE = -0.224 kcal/mol, best = -0.287). Full per-conformer numbers + methodology in
benchmark/nnxtb/RESULTS.md(commit 018353e).