Match state-of-the-art force accuracy on rMD17 aspirin with a 1000-configuration training budget

The revised MD17 (rMD17) benchmark provides recomputed DFT energies and forces (PBE/def2-SVP, tight convergence, dense grid) for 100000 molecular-dynamics snapshots of each of ten small organic molecules (aspirin, azobenzene, benzene, ethanol, malonaldehyde, naphthalene, paracetamol, salicylic acid, toluene, uracil). Using the canonical low-data protocol of training on 950 configurations plus 50 for validation (1000 total) drawn from one of the five official rMD17 train/test index splits, train a machine-learning interatomic potential for the ASPIRIN system and report the per-atom force mean absolute error (MAE) and energy MAE on a held-out test set of at least 1000 configurations from the same split. Aspirin is the hardest of the ten molecules (21 atoms, most conformational flexibility). Can an agent-built model reach a force MAE of $\le 6.6$ meV/$\text{\AA}$ (the reported MACE level) at this budget, and how low can it go?

Acceptance. FULLY RESOLVES: a trained MLIP for rMD17 aspirin using exactly 950 train + 50 validation configurations from a single named official split, achieving test force MAE $\le 6.6$ meV/$\text{\AA}$ over $\ge 1000$ held-out configurations, reported with energy MAE, the split id used, model/hyperparameters, and a runnable training+evaluation script and trained weights. PARTIAL: any reproducible result within a factor of ~2 of that target (force MAE $\le 13$ meV/$\text{\AA}$) at the same 1000-config budget with full protocol disclosure, or a documented negative result showing a specified architecture cannot reach it. Metric definitions: force MAE averaged over all atoms and Cartesian components in meV/$\text{\AA}$; energy MAE in meV per configuration.