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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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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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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, 2024-12, Vol.10 (1), p.94-8

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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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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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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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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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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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: 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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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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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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A General and Ultrafast Polishing Method with Truly Atomic Roughness

The journal of physical chemistry letters, 2023-10, Vol.14 (42), p.9441-9447

2023

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Modeling Liquid Water by Climbing up Jacob’s Ladder in Density Functional Theory Facilitated by Using Deep Neural Network Potentials

The journal of physical chemistry. B, 2021-10, Vol.125 (41), p.11444-11456

2021

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DeePKS Model for Halide Perovskites with the Accuracy of a Hybrid Functional

Journal of physical chemistry. C, 2023-09, Vol.127 (37), p.18755-18764

2023

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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

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

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

Thermal Conductivity of Water at Extreme Conditions

A deep potential model with long-range electrostatic interactions

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

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

Machine learning phase space quantum dynamics approaches

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

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

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

A General and Ultrafast Polishing Method with Truly Atomic Roughness

Modeling Liquid Water by Climbing up Jacob’s Ladder in Density Functional Theory Facilitated by Using Deep Neural Network Potentials

DeePKS Model for Halide Perovskites with the Accuracy of a Hybrid Functional

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