596 citations · 986 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Efficient and Precise Force Field Optimization for Biomolecules Using DPA-2
Junhan Chang, Duo Zhang, Yuqing Deng +5
Molecular simulations are essential tools in computational chemistry, enabling the prediction and understanding of molecular interactions and thermodynamic properties of biomolecul…
DPA-2: a large atomic model as a multi-task learner
Duo Zhang, Xinzijian Liu, Xiangyu Zhang +40
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demo…
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…
Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics
Dongdong Wang, Yanze Wang, Junhan Chang +3
Enhanced sampling methods such as metadynamics and umbrella sampling have become essential tools for exploring the configuration space of molecules and materials. At the same time,…