100 citations · 100 across the 1 of their papers we have counts for
2 papers
physics.chem-ph2025★ 100 cited
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…
cond-mat.mtrl-sci2023
Deep Learning Illuminates Spin and Lattice Interaction in Magnetic Materials
Teng Yang, Zefeng Cai, Zhengtao Huang +10
Atomistic simulations hold significant value in clarifying crucial phenomena such as phase transitions and energy transport in materials science. Their success stems from the prese…