1 citations · 1 across the 2 of their papers we have counts for
6 papers
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…
QT-Net: Rethinking Evaluation of AI Models in Atomic Chemical Space
Pablo MartÃnez Crespo, Stefano Ribes, Martin Rahm +6
Atomic properties such as partial charges or multipoles encode chemically meaningful information that can inform downstream molecular property prediction, but their evaluation as m…
OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
Emily Jin, Andrei Cristian Nica, Mikhail Galkin +8
Accurately predicting experimentally realizable 3D molecular crystal structures from their 2D chemical graphs is a long-standing open challenge in computational chemistry called cr…
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…
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…
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…