363 citations · 898 across the 14 of their papers we have counts for
14 papers
dpti: An Automated Thermodynamic Integration Workflow for Phase Diagram Calculations with Machine Learning Interatomic Potentials
Fengbo Yuan, Xin Zhong, Donghao Zheng +4
Thermodynamic integration (TI) is a widely used approach for computing free energies and phase diagrams. However, TI calculations driven by machine learning interatomic potentials…
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…
Revealing the molecular structures of a-Al2O3(0001)-water interface by machine learning based computational vibrational spectroscopy
Xianglong Du, Weizhi Shao, Chenglong Bao +3
Solid-water interfaces are crucial to many physical and chemical processes and are extensively studied using surface-specific sum-frequency generation (SFG) spectroscopy. To establ…
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…
Thermal Conductivity of Water at Extreme Conditions
Cunzhi Zhang, Marcello Puligheddu, Linfeng Zhang +2
Measuring the thermal conductivity () of water at extreme conditions is a challenging task and few experimental data are available. We predict for temperatures and pressures…
Hybrid Auxiliary Field Quantum Monte Carlo for Molecular Systems
Yixiao Chen, Linfeng Zhang, Weinan E +1
We propose a quantum Monte Carlo approach to solve the ground state many-body Schrodinger equation for the electronic ground state. The method combines optimization from variationa…
Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids
Pei Ge, Linfeng Zhang, Huan Lei
A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and conti…
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,…
Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics
Yuzhi Zhang, Chang Gao, Linfeng Zhang +2
Simulating warm dense matter that undergoes a wide range of temperatures and densities is challenging. Predictive theoretical models, such as quantum-mechanics-based first-principl…
Deep learning inter-atomic potential model for accurate irradiation damage simulations
Hao Wang, Xun Guo, Linfeng Zhang +2
We propose a hybrid scheme that interpolates smoothly the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a newly developed deep learning potential energy…
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…