activity
20222025
most citedThe Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

324 citations · 494 across the 4 of their papers we have counts for

collaborators

5 papers

physics.chem-ph2025★ 1 cited

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms

Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque +24

Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics. However, CSP remains challenging and computationally intensiv…

cs.LG2025

UMA: A Family of Universal Models for Atoms

Brandon M. Wood, Misko Dzamba, Xiang Fu +15

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 includi…

physics.chem-ph2025★ 70 cited

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

Daniel S. Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith +20

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 thi…

cond-mat.mtrl-sci2024★ 99 cited

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu +6

The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing…

cond-mat.mtrl-sci2022★ 324 cited

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

Richard Tran, Janice Lan, Muhammed Shuaibi +14

The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials…