From the 1 of 4 linked papers with an AI index.
4 papers
Ai2-Kit: Streamlining AI-Accelerated Ab Initio Workflows for Complex Chemical Systems
Sheng Bi, Wei-Hong Xu, Yong-Bin Zhuang +47
The paper introduces ai2-kit, a software toolkit that streamlines AI‑accelerated ab initio workflows for complex chemical systems by providing command‑line and Python interfaces fo…
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…
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 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…