10 citations · 30 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Daniel Levy, Siba Smarak Panigrahi, Sékou-Oumar Kaba +5
Generating novel crystalline materials has the potential to lead to advancements in fields such as electronics, energy storage, and catalysis. The defining characteristic of crysta…
Deconstructing equivariant representations in molecular systems
Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret
Recent equivariant models have shown significant progress in not just chemical property prediction, but as surrogates for dynamical simulations of molecules and materials. Many of…
Towards Foundation Models for Materials Science: The Open MatSci ML Toolkit
Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings +3
Artificial intelligence and machine learning have shown great promise in their ability to accelerate novel materials discovery. As researchers and domain scientists seek to unify a…
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar +3
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.…