5 papers
MinSurf: resolving the atomic-scale stability landscape of mineral surfaces
Fengzijun Pan, Zhoulin Liu, Pingyang Zhang +4
Mineral surfaces govern interfacial reactivity in carbon mineralization, geo-energy storage, contaminant immobilization, heterogeneous catalysis and electrochemical interface engin…
ORION: Unifying Top-Down and Bottom-Up Chemical Space Sampling for a Universal Organic Force Field
Zherui Chen, Jiayu Zhang, Yuxuan Tian +7
Empirical force fields remain the primary tool for large-scale molecular simulation, yet their limited flexibility and transferability often hinder predictive modeling in chemicall…
GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP
Zihan Yan, Denan Li, Xin Wu +20
Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics proce…
qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations
Zheyong Fan, Benrui Tang, Esmée Berger +13
Although electrostatics can be incorporated into machine-learned interatomic potentials, existing approaches are computationally very demanding, limiting large-scale, long-time sim…
NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials
Chengbing Chen, Yutong Li, Rui Zhao +4
As a machine-learned potential, the neuroevolution potential (NEP) method features exceptional computational efficiency and has been successfully applied in materials science. Cons…