596 citations · 917 across the 14 of their papers we have counts for
14 papers
Deep variational free energy prediction of dense hydrogen solid at 1200K
Xinyang Dong, Hao Xie, Yixiao Chen +4
We perform deep variational free energy calculations to investigate the dense hydrogen system at 1200 K and high pressures. In this computational framework, neural networks are use…
Efficient Numerical Algorithm for Large-Scale Damped Natural Gradient Descent
Yixiao Chen, Hao Xie, Han Wang
We propose a new algorithm for efficiently solving the damped Fisher matrix in large-scale scenarios where the number of parameters significantly exceeds the number of available sa…
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…
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…
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…
Topology of vibrational modes predict plastic events in glasses
Zhen Wei Wu, Yixiao Chen, Wei-Hua Wang +2
The plastic deformation of crystalline materials can be understood by considering their structural defects such as disclinations and dislocations. Although glasses are also solids,…
DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials
Wenfei Li, Qi Ou, Yixiao Chen +9
Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanica…
Ab Initio bulk free energy surface of proper ferroelectrics
Pinchen Xie, Yixiao Chen, Xinyu Xu +3
We report a systematic and accurate approach for deriving the bulk free energy surface (FES), a function of temperature, polarization, and strain, from the first-principles density…
Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms
Zhuoqiang Guo, Denghui Lu, Yujin Yan +11
High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of \textit{ab initio} m…
DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models
Denghui Lu, Wanrun Jiang, Yixiao Chen +4
Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structur…
DeePKS-kit: a package for developing machine learning-based chemically accurate energy and density functional models
Yixiao Chen, Linfeng Zhang, Han Wang +1
We introduce DeePKS-kit, an open-source software package for developing machine learning based energy and density functional models. DeePKS-kit is interfaced with PyTorch, an open-…
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory
Yixiao Chen, Linfeng Zhang, Han Wang +1
We propose a general machine learning-based framework for building an accurate and widely-applicable energy functional within the framework of generalized Kohn-Sham density functio…
Ground state energy functional with Hartree-Fock efficiency and chemical accuracy
Yixiao Chen, Linfeng Zhang, Han Wang +1
We introduce the Deep Post-Hartree-Fock (DeePHF) method, a machine learning based scheme for constructing accurate and transferable models for the ground-state energy of electronic…
Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks
Leonardo Zepeda-Núñez, Yixiao Chen, Jiefu Zhang +3
The recently developed Deep Potential [Phys. Rev. Lett. 120, 143001, 2018] is a powerful method to represent general inter-atomic potentials using deep neural networks. The success…