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.
AUTHORS
Written by
Xiang Fu
Kareem Abdelmaqsoud
John R. Kitchin
Abhishek Das
Ammar Rizvi
Anuroop Sriram
Brandon M. Wood
Daniel S. Levine
Sushree Jagriti Sahoo
Kyle Michel
C. Lawrence Zitnick
Luis Barroso-Luque
Misko Dzamba
Muhammed Shuaibi
Meng Gao
Taco Cohen
Vahe Gharakhanyan
Zachary W. Ulissi
Publisher
arXiv
Research Topics
Core Machine Learning
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