The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models

Understand

Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost.

• Realization of this potential would enable high-throughout, high-accuracy molecular screening campaigns to explore vast regions of chemical space and facilitate ab initio simulations at sizes and time scales that were previously inaccessible.
• However, a fundamental challenge to creating ML models that perform well across molecular chemistry is the lack of comprehensive data for training.
• Despite substantial efforts in data generation, no large-scale molecular dataset exists that combines broad chemical diversity with a high level of accuracy.