596 citations · 736 across the 6 of their papers we have counts for
6 papers
Ai2-Kit: Streamlining AI-Accelerated Ab Initio Workflows for Complex Chemical Systems
Sheng Bi, Wei-Hong Xu, Yong-Bin Zhuang +47
Molecular simulations of complex chemical systems, such as catalysis, electrochemistry, and energy storage, often need to capture the interplay of effects such as electronic struct…
Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces
Lucas B. T. de Kam, Jia-Xin Zhu, Ankit Mathanker +2
Atomistic simulations of electrochemical interfaces remain challenging due to the long time scales required to adequately sample the structure of the electric double layer. The eme…
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Jinzhe Zeng, Duo Zhang, Anyang Peng +44
In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for m…
Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response
Jia-Xin Zhu, Jun Cheng
Understanding electrochemical interfaces at a microscopic level is essential for elucidating important electrochemical processes in electrocatalysis, batteries and corrosion. While…
Observation of dendrite formation at Li metal-electrolyte interface: A machine-learning enhanced constant potential framework
Taiping Hu, Haichao Huang, Guobing Zhou +8
Uncontrollable dendrites growth during electrochemical cycles leads to low Coulombic efficiency and critical safety issues in Li metal batteries. Hence, a comprehensive understandi…
DeePMD-kit v2: A software package for Deep Potential models
Jinzhe Zeng, Duo Zhang, Denghui Lu +44
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. T…