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20222026
most citedEGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations

2 citations · 6 across the 7 of their papers we have counts for

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cond-mat.mtrl-sci2025

UniFFBench: Evaluating Universal Machine Learning Force Fields Against Experimental Measurements

Sajid Mannan, Vaibhav Bihani, Carmelo Gonzales +5

Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table. However, their evalua…

cond-mat.mtrl-sci2025★ 1 cited

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials

Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales +2

Universal Machine Learning Interactomic Potentials (MLIPs) enable accelerated simulations for materials discovery. However, current research efforts fail to impactfully utilize MLI…