Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials
Benjamin W. J. Chen, Xinglong Zhang, Jia Zhang
Abstract
Active learning of machine learning interatomic potentials enables dynamic modelling of adsorption and reactions at explicitly solvated heterogeneous catalyst interfaces with near ab initio accuracy and greatly reduced computational cost.
Topics & Concepts
Molecular dynamicsCatalysisAb initioChemistrySolventEthylene glycolAdsorptionChemical physicsComputational chemistrySolvent effectsInteratomic potentialMaterials sciencePhysical chemistryOrganic chemistryMachine Learning in Materials ScienceSurface Chemistry and CatalysisCatalytic Processes in Materials Science