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Solving many-electron Schrödinger equation using deep neural networks

Journal of computational physics, 2019-12, Vol.399, p.108929, Article 108929

2019

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Phase Diagram of a Deep Potential Water Model

Physical review letters, 2021-06, Vol.126 (23), p.1-236001, Article 236001

2021

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DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

Computer physics communications, 2020-08, Vol.253 (C), p.107206, Article 107206

2020

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Highly efficient and damage-free polishing of GaN (0 0 0 1) by electrochemical etching-enhanced CMP process

Applied surface science, 2020-06, Vol.514, p.145957, Article 145957

2020

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DeePCG: Constructing coarse-grained models via deep neural networks

The Journal of chemical physics, 2018-07, Vol.149 (3), p.034101-034101

2018

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Accurate Deep Potential model for the Al–Cu–Mg alloy in the full concentration space

Chinese physics B, 2021-05, Vol.30 (5), p.50706-21

2021

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Deep learning inter-atomic potential model for accurate irradiation damage simulations

Applied physics letters, 2019-06, Vol.114 (24)

2019

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Deep neural network for the dielectric response of insulators

Physical review. B, 2020-07, Vol.102 (4), p.1, Article 041121

2020

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Isotope effects in liquid water via deep potential molecular dynamics

Molecular physics, 2019-11, Vol.117 (22), p.3269-3281

2019

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Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional

Physical review. B, 2020-12, Vol.102 (21), p.1, Article 214113

2020

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m^$ of two-dimensional electron gas: A neural canonical transformation study

SciPost physics, 2023-06, Vol.14 (6), p.154, Article 154

2023

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Neural Canonical Transformation with Symplectic Flows

Physical review. X, 2020-04, Vol.10 (2), p.021020, Article 021020

2020

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DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Computer physics communications, 2018-07, Vol.228 (C), p.178-184

2018

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Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Physical review letters, 2018-04, Vol.120 (14), p.143001-143001, Article 143001

2018

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86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

Computer physics communications, 2021-02, Vol.259 (C), p.107624, Article 107624

2021

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Reinforced dynamics for enhanced sampling in large atomic and molecular systems

The Journal of chemical physics, 2018-03, Vol.148 (12), p.124113-124113

2018

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Theoretical and experimental study on plasma-induced atom-migration manufacturing (PAMM) of glass

Applied surface science, 2022-10, Vol.599, p.153976, Article 153976

2022

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A deep potential model with long-range electrostatic interactions

The Journal of chemical physics, 2022-03, Vol.156 (12), p.124107-124107

2022

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DP Compress: A Model Compression Scheme for Generating Efficient Deep Potential Models

Journal of chemical theory and computation, 2022-09, Vol.18 (9), p.5559-5567

2022

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Highly efficient and atomic scale polishing of GaN via plasma-based atom-selective etching

Applied surface science, 2023-05, Vol.620, p.156786, Article 156786

2023

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Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2020-09, Vol.124 (35), p.7155-7165

2020

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Deep learning of accurate force field of ferroelectric HfO2

Physical review. B, 2021-01, Vol.103 (2), p.024108

2021

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DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory

Journal of chemical theory and computation, 2021-01, Vol.17 (1), p.170-181

2021

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Machine learning phase space quantum dynamics approaches

The Journal of chemical physics, 2021-05, Vol.154 (18), p.184104-184104

2021

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DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models

Computer physics communications, 2023-01, Vol.282 (C), p.108520, Article 108520

2023

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Solving many-electron Schrödinger equation using deep neural networks

Phase Diagram of a Deep Potential Water Model

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

Highly efficient and damage-free polishing of GaN (0 0 0 1) by electrochemical etching-enhanced CMP process

DeePCG: Constructing coarse-grained models via deep neural networks

Accurate Deep Potential model for the Al–Cu–Mg alloy in the full concentration space

Deep learning inter-atomic potential model for accurate irradiation damage simulations

Deep neural network for the dielectric response of insulators

Isotope effects in liquid water via deep potential molecular dynamics

Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional

m^$ of two-dimensional electron gas: A neural canonical transformation study

Neural Canonical Transformation with Symplectic Flows

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

Reinforced dynamics for enhanced sampling in large atomic and molecular systems

Theoretical and experimental study on plasma-induced atom-migration manufacturing (PAMM) of glass

A deep potential model with long-range electrostatic interactions

DP Compress: A Model Compression Scheme for Generating Efficient Deep Potential Models

Highly efficient and atomic scale polishing of GaN via plasma-based atom-selective etching

Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy

Deep learning of accurate force field of ferroelectric HfO2

DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory

Machine learning phase space quantum dynamics approaches

DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models

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