170 citations · 412 across the 22 of their papers we have counts for
5 papers · 1 filter
Simulations with machine learning potentials identify the ion conduction mechanism mediating non-Arrhenius behavior in LGPS
Gavin Winter, Rafael Gómez-Bombarelli
LiGe(PS) (LGPS) is a highly concentrated solid electrolyte, in which Coulombic repulsion between neighboring cations is hypothesized as the underlying reason for con…
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
Xiang Fu, Zhenghao Wu, Wujie Wang +4
Molecular dynamics (MD) simulation techniques are widely used for various natural science applications. Increasingly, machine learning (ML) force field (FF) models begin to replace…
Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity
Sheng Gong, Tian Xie, Yang Shao-Horn +2
Historically, materials informatics has relied on human-designed descriptors of materials structures. In recent years, graph neural networks (GNNs) have been proposed for learning…
Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential
Simon Axelrod, Eugene Shakhnovich, Rafael Gomez-Bombarelli
Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life…
From Free-Energy Profiles to Activation Free Energies
Johannes C. B. Dietschreit, Dennis J. Diestler, Andreas Hulm +2
Given a chemical reaction going from reactant (R) to the product (P) on a potential energy surface (PES) and a collective variable (CV) that discriminates between R and P, one can…