46 citations · 75 across the 18 of their papers we have counts for
5 papers · 1 filter
Coarse-Grained Dynamics with Spatial Disorder and Non-Markovian Memory
Chuyi Liu, Yifeng Guan, Jingyuan Li +1
We introduce the spatial disorder-generalized Langevin equation (SD-GLE), a data-driven method for constructing coarse-grained (CG) dynamics in heterogeneous systems. Unlike conven…
Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression
Yifeng Guan, Chuyi Liu, Dongzhan Zhou +4
Discovering interpretable physical laws from high-dimensional data is a fundamental challenge in scientific research. Traditional methods, such as symbolic regression, often produc…
Iterative Pretraining Framework for Interatomic Potentials
Taoyong Cui, Zhongyao Wang, Dongzhan Zhou +5
Machine learning interatomic potentials (MLIPs) enable efficient molecular dynamics (MD) simulations with ab initio accuracy and have been applied across various domains in physica…
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…
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…