363 citations · 898 across the 14 of their papers we have counts for
7 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…
DeePKS Model for Halide Perovskites with the Accuracy of Hybrid Functional
Qi Ou, Ping Tuo, Wenfei Li +3
Accurate prediction for the electronic structure properties of halide perovskites plays a significant role in the design of highly efficient and stable solar cells. While density f…
Modeling liquid water by climbing up Jacob's ladder in density functional theory facilitated by using deep neural network potentials
Chunyi Zhang, Fujie Tang, Mohan Chen +5
Within the framework of Kohn-Sham density functional theory (DFT), the ability to provide good predictions of water properties by employing a strongly constrained and appropriately…
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,…
The Phase Diagram of a Deep Potential Water Model
Linfeng Zhang, Han Wang, Roberto Car +1
Using the Deep Potential methodology, we construct a model that reproduces accurately the potential energy surface of the SCAN approximation of density functional theory for water,…
Adaptive coupling of a deep neural network potential to a classical force field
Linfeng Zhang, Han Wang, Weinan E
An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the mol…
DeePCG: constructing coarse-grained models via deep neural networks
Linfeng Zhang, Jiequn Han, Han Wang +2
We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- and/or three-body contributio…