Uploaded May 2025 | Updated September 2026, 2 days ago
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.
We’re also sharing Meta’s Universal Model for Atoms (UMA), a machine learning interatomic potential that sets new standards for modeling the interaction of atoms across a wide range of materials and molecules. UMA is trained on over 30 billion atoms contained in all of the datasets released by Meta in the past five years, including those with both molecules and materials. UMA offers researchers a foundational model that provides more accurate predictions and improved understanding of molecular behavior and serves as a versatile base for downstream use cases and fine-tuning applications.
Together, OMol25 and UMA have the potential to unlock new capabilities in molecular and materials research. At Meta, we see this as the next step in a long journey of open science releases to accelerate atomic-scale materials design. We’re also working with partners like the Lawrence Livermore National Laboratory to extend these datasets and models to new classes of molecules, such as polymers.
Read more: ai.meta.com/blog/meta-fair-science-new-open-source-releases
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Meta focuses on bringing the world together by advancing AI, powering meaningful and safe experiences, and conducting open research.
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.
We’re also sharing Meta’s Universal Model for Atoms (UMA), a machine learning interatomic potential that sets new standards for modeling the interaction of atoms across a wide range of materials and molecules. UMA is trained on over 30 billion atoms contained in all of the datasets released by Meta in the past five years, including those with both molecules and materials. UMA offers researchers a foundational model that provides more accurate predictions and improved understanding of molecular behavior and serves as a versatile base for downstream use cases and fine-tuning applications.
Together, OMol25 and UMA have the potential to unlock new capabilities in molecular and materials research. At Meta, we see this as the next step in a long journey of open science releases to accelerate atomic-scale materials design. We’re also working with partners like the Lawrence Livermore National Laboratory to extend these datasets and models to new classes of molecules, such as polymers.
Read more: ai.meta.com/blog/meta-fair-science-new-open-source-releases
Subscribe: youtube.com/aiatmeta?sub_confirmation=1
Learn more about our work: ai.meta.com
Follow us on Twitter: twitter.com/aiatmeta
Follow us on Facebook: facebook.com/aiatmeta
Connect with us on LinkedIn: linkedin.com/showcase/aiatmeta
Meta focuses on bringing the world together by advancing AI, powering meaningful and safe experiences, and conducting open research.
