596 citations · 696 across the 3 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2025
Benchmarking Universal Interatomic Potentials on Zeolite Structures
Shusuke Ito, Koki Muraoka, Akira Nakayama
Interatomic potentials (IPs) with wide elemental coverage and high accuracy are powerful tools for high-throughput materials discovery. While the past few years witnessed the devel…
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…
physics.chem-ph2023★ 596 cited
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…