23 citations · 23 across the 3 of their papers we have counts for
3 papers
physics.comp-ph2024
Evidential Deep Learning for Interatomic Potentials
Han Xu, Taoyong Cui, Chenyu Tang +8
Machine learning interatomic potentials (MLIPs) have been widely used to facilitate large-scale molecular simulations with accuracy comparable to ab initio methods. In practice, ML…
physics.comp-ph2024
Online Test-time Adaptation for Interatomic Potentials
Taoyong Cui, Chenyu Tang, Dongzhan Zhou +5
Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physic…
physics.chem-ph2023★ 23 cited
Geometry-enhanced Pre-training on Interatomic Potentials
Taoyong Cui, Chenyu Tang, Mao Su +6
Machine learning interatomic potentials (MLIPs) enables molecular dynamics (MD) simulations with ab initio accuracy and has been applied to various fields of physical science. Howe…