Reach chemical accuracy on COMP6 relative energies with a potential trained only on ANI-1x

A central test of an ML interatomic potential is transferability: accuracy on molecules larger and more diverse than its training set. ANI-1x provides about 5 million DFT (wB97x/6-31G(d)) energies and forces for off-equilibrium conformers of organic molecules containing H, C, N, O, generated by active learning. COMP6 is the paired out-of-distribution benchmark suite of six subsets (GDB7to9, GDB10to13, DrugBank, Tripeptides, ANI-MD, and S66x8) with the same DFT reference. Train a potential using ONLY ANI-1x data, then evaluate on COMP6 and report the relative-conformer-energy MAE/RMSE (kcal/mol) for each subset. The DrugBank and Tripeptide subsets contain molecules substantially larger than typical ANI-1x training molecules, so they probe size extrapolation. Can an agent-built model trained on ANI-1x reach chemical accuracy (relative-energy MAE $\le 1$ kcal/mol) on the larger COMP6 subsets, or does error grow systematically with molecule size?

Acceptance. FULLY RESOLVES: a potential trained only on ANI-1x (no COMP6 or larger-molecule data leaked in) achieving relative-conformer-energy MAE $\le 1$ kcal/mol on the DrugBank and Tripeptide COMP6 subsets, reported with per-subset MAE/RMSE for all six subsets, the exact training protocol, model/hyperparameters, and a runnable training+evaluation script and weights. PARTIAL: any reproducible ANI-1x-trained model reporting per-subset COMP6 relative-energy errors (even above chemical accuracy), or a documented negative result quantifying how error scales with molecule size across the subsets. Metric: MAE and RMSE of relative conformer energies (each conformer referenced to its molecule's mean or lowest energy) in kcal/mol, per COMP6 subset.