4 papers
Benchmarking Universal Machine-Learned Interatomic Potentials for High-Temperature Metal-Organic Framework Chemistry
Connor W. Edwards, Jack D. Evans
Universal machine-learned interatomic potentials (uMLIPs) offer a promising approach to performing atomistic simulations at near-DFT accuracy with greatly reduced computational cos…
MLIP-MC: A Framework for Adsorption Simulations using Machine-Learned Interatomic Potentials
Connor W. Edwards, Fengxu Yang, Konstantin Stracke +1
Grand canonical Monte Carlo (GCMC) simulations are essential for screening metal-organic frameworks (MOFs) for gas adsorption, yet their accuracy is limited by underlying interatom…
Simulations of High Temperature Decomposition of Metal-Organic Frameworks to form Amorphous Catalysts
Connor W. Edwards, Oliver M. Linder-Patton, Jack D. Evans
Metal-organic framework (MOF) derived materials formed through high temperature processes show great potential as catalysts. However, understanding of structure-property relationsh…
Evaluating Mechanical Property Prediction across Material Classes using Molecular Dynamics Simulations with Universal Machine-Learned Interatomic Potentials
Konstantin Stracke, Connor W. Edwards, Jack D. Evans
We assess the accuracy of six universal machine-learned interatomic potentials (MLIPs) for predicting the temperature and pressure response of materials by molecular dynamics simul…