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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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Raman spectrum and polarizability of liquid water from deep neural networks

Physical chemistry chemical physics : PCCP, 2020-05, Vol.22 (19), p.1592-162

2020

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Viscosity in water from first-principles and deep-neural-network simulations

npj computational materials, 2022-07, Vol.8 (1), p.1-10, Article 139

2022

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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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Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

npj computational materials, 2021-12, Vol.7 (1), p.1-11, Article 206

2021

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Pretraining of attention-based deep learning potential model for molecular simulation

npj computational materials, 2024-05, Vol.10 (1), p.94-8, Article 94

2024

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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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Thermal Conductivity of Water at Extreme Conditions

The journal of physical chemistry. B, 2023-07, Vol.127 (31), p.7011-7017

2023

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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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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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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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One-pot synthesized NiFe2O4/CeO2 composite catalyst for efficient degradation of methylene blue via photocatalysis under visible light

Catalysis communications, 2023-12, Vol.185, p.106814, Article 106814

2023

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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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Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors

The Journal of chemical physics, 2021-03, Vol.154 (9), p.094703-094703

2021

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Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids

The Journal of chemical physics, 2023-02, Vol.158 (6), p.064104-064104

2023

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A study of thermal etching of GaN by atmospheric argon inductively coupled plasma

Thermochimica acta, 2023-06, Vol.724, p.179491, Article 179491

2023

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Efficiently Trained Deep Learning Potential for Graphane

Journal of physical chemistry. C, 2021-07, Vol.125 (27), p.14874-14882

2021

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Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model

Journal of chemical theory and computation, 2024-07, Vol.20 (13), p.5717-5731

2024

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Revealing the molecular structures of α-Al2O3(0001)–water interface by machine learning based computational vibrational spectroscopy

The Journal of chemical physics, 2024-09, Vol.161 (12)

2024

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Interface Engineering of the Cu1.5Mn1.5O4/CeO2 Heterostructure for Highly Efficient Electrocatalytic Nitrate Reduction to Ammonia

Nano letters, 2024-07, Vol.24 (29), p.8964-8972

2024

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When do short-range atomistic machine-learning models fall short?

The Journal of chemical physics, 2021-01, Vol.154 (3), p.034111-034111

2021

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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

Raman spectrum and polarizability of liquid water from deep neural networks

Viscosity in water from first-principles and deep-neural-network simulations

Isotope effects in liquid water via deep potential molecular dynamics

Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

Pretraining of attention-based deep learning potential model for molecular simulation

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

Thermal Conductivity of Water at Extreme Conditions

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

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

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

One-pot synthesized NiFe2O4/CeO2 composite catalyst for efficient degradation of methylene blue via photocatalysis under visible light

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

Machine learning phase space quantum dynamics approaches

Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors

Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids

A study of thermal etching of GaN by atmospheric argon inductively coupled plasma

Efficiently Trained Deep Learning Potential for Graphane

Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model

Revealing the molecular structures of α-Al2O3(0001)–water interface by machine learning based computational vibrational spectroscopy

Interface Engineering of the Cu1.5Mn1.5O4/CeO2 Heterostructure for Highly Efficient Electrocatalytic Nitrate Reduction to Ammonia

When do short-range atomistic machine-learning models fall short?

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