10 citations · 30 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…