Researchmlipmolecular modelinggeometry optimization
MLIP Models Produce Approximate Molecular Geometries
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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.
Scoring Rationale
Large-scale dataset and practical MLIP applications drive high impact; preprint status and lack of peer review limit credibility.
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