MLIP Models Produce Approximate Molecular Geometries

A Feb. 24, 2026 arXiv preprint curates a 3.5 million-molecule relaxation dataset with 300 million snapshots and trains ML interatomic potential (MLIP) models to predict energies and forces. The authors apply the pre-trained models for geometry optimization and direct fine-tuning for property prediction, finding relaxed geometries—while not consistently reaching DFT chemical accuracy—improve downstream property performance. Code and data are released.