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

TL;DR

The paper introduces OMol25, a large-scale, diverse DFT dataset with over 100 million calculations, designed to advance machine learning models for molecular chemistry by providing extensive, high-accuracy training data.

Contribution

It presents OMol25, the largest and most diverse molecular dataset to date, along with baseline models and evaluations to foster ML development in chemistry.

Findings

OMol25 contains over 100 million DFT calculations.

The dataset covers 83 elements and diverse molecular structures.

Baseline models demonstrate the dataset's utility for ML applications.

Abstract

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. To address this gap, Meta FAIR introduces Open Molecules 2025 (OMol25), a large-scale dataset composed of more than 100 million density functional theory (DFT) calculations at the $\omega$B97M-V/def2-TZVPD level of theory, representing billions of CPU core-hours of compute. OMol25 uniquely blends elemental, chemical, and structural diversity including: 83 elements, a wide-range of intra- and intermolecular interactions, explicit solvation, variable charge/spin, conformers, and reactive structures. There are ~83M unique molecular systems in OMol25 covering small molecules, biomolecules, metal complexes, and electrolytes, including structures obtained from existing datasets. OMol25 also greatly expands on the size of systems typically included in DFT datasets, with systems of up to 350 atoms. In addition to the public release of the data, we provide baseline models and a comprehensive set of model evaluations to encourage community engagement in developing the next-generation ML models for molecular chemistry.