RESEARCH

CORE MACHINE LEARNING

UMA: A Family of Universal Models for Atoms

May 14, 2025

Abstract

The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science including drug discovery, energy storage, and semiconductor manufacturing. To address this need, Meta FAIR presents a family of Universal Models for Atoms UMA, designed to push the frontier of speed, accuracy, and generalization. UMA models are trained on half a billion unique 3D atomic structures (the largest training runs to date) by compiling data across multiple chemical domains, e.g. molecules, materials, and catalysts. We develop empirical scaling laws to help understand how to increase model capacity alongside dataset size to achieve the best accuracy. The UMA small and medium models utilize a novel architectural design we refer to as mixture of linear experts that enables increasing model capacity without sacrificing speed. For example, UMA-medium has 1.4B parameters but only ~50M active parameters per atomic structure. We evaluate UMA models on a diverse set of tasks across multiple domains and find that, remarkably, a single model without any fine-tuning can perform similarly or better than specialized models. We are releasing the UMA code, weights, and associated data to accelerate computational workflows and enable the community to continue to build increasingly capable AI models.

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AUTHORS

Written by

Brandon M. Wood

Misko Dzamba

Xiang Fu

Meng Gao

Muhammed Shuaibi

Luis Barroso-Luque

Kareem Abdelmaqsoud

Vahe Gharakhanyan

John R. Kitchin

Daniel S. Levine

Kyle Michel

Anuroop Sriram

Taco Cohen

Abhishek Das

Ammar Rizvi

Sushree Jagriti Sahoo

Zachary W. Ulissi

C. Lawrence Zitnick

Publisher

arXiv

Research Topics

Core Machine Learning

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