We’re excited to release a new Density Functional Theory (DFT) dataset, Open Molecules 2025 (OMol25), that extends the family of Meta’s open science simulation datasets—which include Open Catalyst 2020-2022, Open DAC 2023, and Open Materials 2024—to molecular chemistry. Foundational quantum chemistry methods like DFT can be used to predict properties of molecules and materials at the atomic-level scale, especially in complex scenarios where chemical bonds are breaking and forming.

As the largest and most diverse dataset of high-accuracy quantum chemistry calculations for biomolecules, metal complexes, and electrolytes, OMol25 enables unprecedented accuracy in atomic-scale design in healthcare and energy storage technologies.
Previous molecular datasets were much smaller, with simulations that only included 20 to 30 atoms and limited elements. Requiring 6 billion core hours of compute, the OMol25 dataset is a major leap forward with configurations up to 10 times larger, including complex interactions between many different elements.

Last week, Meta's Fundamental AI Research (FAIR) team released Open Molecules 2025 (OMol25), a massive dataset of high-accuracy computational chemistry calculations. In addition to the dataset, they released several pre-trained neural network potentials (NNPs) trained on the dataset for use in molecular modeling, including a new "Universal Model for Atoms" (UMA) that unifies OMol25 with other datasets from the FAIR-chem team.
We've been following this space pretty closely for a while, and I (Corin) had the privilege of being one of the few non-Meta chemists in the room when these models were announced at the Meta Open-Source AI conference in Austin. It was immediately obvious, just from the first few slides, that this would go down as one of the most impactful scientific releases of the year—and that the "ML for atomistic simulation" field would be divided into "pre-OMol25" and "post-OMol25" eras.