3 papers
cond-mat.mtrl-sci2026
Trillion-atom molecular dynamics simulations with ab initio accuracy
Pengfei Suo, Wudi Cao, Xingxing Wu +14
Material properties are fundamentally dictated by multiscale phenomena, which often reach mesoscale in size. The μm mesoscale is also the size which can be observed directly under…
cs.DC2025
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs
Yuanchang Zhou, Siyu Hu, Chen Wang +3
Graph neural network universal interatomic potentials (GNN-UIPs) have demonstrated remarkable generalization and transfer capabilities in material discovery and property prediction…
physics.comp-ph2024
ALKPU: an active learning method for the DeePMD model with Kalman filter
Haibo Li, Xingxing Wu, Liping Liu +4
Neural network force field models such as DeePMD have enabled highly efficient large-scale molecular dynamics simulations with ab initio accuracy. However, building such models hea…