DeepModeling

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Publications driven by DP-GEN

The following publications have used the DP-GEN software. Publications that only mentioned the DP-GEN will not be included below.

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Last update date: June 30, 2024

2024

Phase Transition in Silicon from Machine Learning Informed Metadynamics

Mangladeep Bhullar, Zihao Bai, Akinwumi Akinpelu, Yansun Yao
Chemphyschem: Eur. J. Chem. Phys. Phys. Chem., 2024, e202400090.
DOI: 10.1002/cphc.202400090

Non-equilibrium nature of fracture determines the crack paths

Pengjie Shi, Shizhe Feng, Zhiping Xu
Extrem. Mech. Lett., 2024, 68, 102151.
DOI: 10.1016/j.eml.2024.102151

Revisiting the phase diagram and piezoelectricity of lead zirconate titanate from first principles

Yubai Shi, Ri He, Bingwen Zhang, Zhicheng Zhong
Phys. Rev. B, 2024, 109 (17), 174104.
DOI: 10.1103/PhysRevB.109.174104

Exploring dielectric properties in atomistic models of amorphous boron nitride

Thomas Galvani, Ali K Hamze, Laura Caputo, Onurcan Kaya, Simon M-M Dubois, Luigi Colombo, Viet-Hung Nguyen, Yongwoo Shin, Hyeon-Jin Shin, Jean-Christophe Charlier, Stephan Roche
J. Phys. Mater., 2024, 7 (3), 35003.
DOI: 10.1088/2515-7639/ad4c06

Double-Layer Distribution of Hydronium and Hydroxide Ions in the Air-Water Interface

Pengchao Zhang, Muye Feng, Xuefei Xu
Acs Phys. Chem Au, 2024.
DOI: 10.1021/acsphyschemau.3c00076

Ultrafast switching dynamics of the ferroelectric order in stacking- engineered ferroelectrics

Ri He, Bingwen Zhang, Hua Wang, Lei Li, Ping Tang, Gerrit Bauer, Zhicheng Zhong
Acta Materialia, 2024, 262, 119416.
DOI: 10.1016/j.actamat.2023.119416

Unraveling pyrolysis mechanisms of lignin dimer model compounds: Neural network-based molecular dynamics simulation investigations

Zhe Shang, Hui Li
Fuel, 2024, 357, 129909.
DOI: 10.1016/j.fuel.2023.129909

2023

Active learning prediction and experimental confirmation of atomic structure and thermophysical properties for liquid Hf_{76}W_{24} refractory alloy

K. L. Liu, R. L. Xiao, Y. Ruan, B. Wei
Phys. Rev., E, 2023, 108 (5-2), 55310.
DOI: 10.1103/PhysRevE.108.055310

Universal interatomic potential for perovskite oxides

Jing Wu, Jiyuan Yang, Yuan-Jinsheng Liu, Duo Zhang, Yudi Yang, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Phys. Rev. B, 2023, 108 (18), L180104.
DOI: 10.1103/PhysRevB.108.L180104

Efficient Molecular Dynamics Simulations of Deep Eutectic Solvents with First-Principles Accuracy Using Machine Learning Interatomic Potentials

Omid Shayestehpour, Stefan Zahn
J. Chem. Theory Comput., 2023, 19 (23), 8732–8742.
DOI: 10.1021/acs.jctc.3c00944

Ferroelectric Domain and Switching Dynamics in Curved In2Se3: First- Principles and Deep Learning Molecular Dynamics Simulations

Dongyu Bai, Yihan Nie, Jing Shang, Junxian Liu, Minghao Liu, Yang Yang, Haifei Zhan, Liangzhi Kou, Yuantong Gu
Nano Lett., 2023, 23 (23), 10922–10929.
DOI: 10.1021/acs.nanolett.3c03160

Data Efficient and Stability Indicated Sampling for Developing Reactive Machine Learning Potential to Achieve Ultralong Simulation in Lithium-Metal Batteries

Longkun Xu, Wei Shao, Haishun Jin, Qiang Wang
J. Phys. Chem. C, 2023, 127 (50), 24106–24117.
DOI: 10.1021/acs.jpcc.3c05522

Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric KNbO3 perovskite

Hao-Cheng Thong, XiaoYang Wang, Jian Han, Linfeng Zhang, Bei Li, Ke Wang, Ben Xu
Phys. Rev. B, 2023, 107, 14101.
DOI: 10.1103/PhysRevB.107.014101

Li ion diffusion behavior of Li3OCl solid-state electrolytes with different defect structures: insights from the deep potential model

Zhou Zhang, Zhongyun Ma, Yong Pei
Phys. Chem. Chem. Phys., 2023, 25, 13297–13307.
DOI: 10.1039/d2cp06073f

A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten

Chang-Jie Ding, Ya-Wei Lei, Xiao-Yang Wang, Xiao-Lin Li, Xiang-Yan Li, Yan-Ge Zhang, Yi-Chun Xu, Chang-Song Liu, Xue-Bang Wu
Tungsten, 2023.
DOI: 10.1007/s42864-023-00230-4

Deep-learning potentials for proton transport in double-sided graphanol

Siddarth K. Achar, Leonardo Bernasconi, Juan J. Alvarez, J. Karl Johnson
Journal of Materials Research, 2023.
DOI: 10.1557/s43578-023-01141-3

Speciation of La3+-Cl- Complexes in Hydrothermal Fluids from Deep Potential Molecular Dynamics

Wei Zhang, Li Zhou, Tinggui Yan, Mohan Chen
J. Phys. Chem. B, 2023, 127, 8926–8937.
DOI: 10.1021/acs.jpcb.3c05428

Neural Network Water Model Based on the MB-Pol Many-Body Potential

Maria Carolina Muniz, Roberto Car, Athanassios Z Panagiotopoulos
J. Phys. Chem. B, 2023, 127, 9165–9171.
DOI: 10.1021/acs.jpcb.3c04629

Insights into the local structure evolution and thermophysical properties of NaCl-KCl-MgCl2\texte ndashLaCl3 melt driven by machine learning

Jia Zhao, Taixi Feng, Guimin Lu, Jianguo Yu
J. Mater. Chem. A, 2023, 11, 23999–24012.
DOI: 10.1039/d3ta03434h

Constrained Hybrid Monte Carlo Sampling Made Simple for Chemical Reaction Simulations

Bin Jin, Taiping Hu, Kuang Yu, Shenzhen Xu
J. Chem. Theory Comput., 2023, 19, 7343–7357.
DOI: 10.1021/acs.jctc.3c00571

Machine learning assisted investigation of the barocaloric performance in ammonium iodide

Xiong Xu, Fangbiao Li, Chang Niu, Min Li, Hui Wang
2023, 122.
DOI: 10.1063/5.0131696

Thermal transport across copper-water interfaces according to deep potential molecular dynamics

Zhiqiang Li, Xiaoyu Tan, Zhiwei Fu, Linhua Liu, Jia-Yue Yang
Phys. Chem. Chem. Phys., 2023, 25, 6746–6756.
DOI: 10.1039/d2cp05530a

Structure and solidification of the (Fe0.75B0.15Si0.1)100-xTax (x=0-2) melts: Experiment and machine learning

I.V. Sterkhova, L.V. Kamaeva, V.I. Lad'yanov, N.M. Chtchelkatchev
Journal of Physics and Chemistry of Solids, 2023, 174, 111143.
DOI: 10.1016/j.jpcs.2022.111143

Unravelling the dissolution dynamics of silicate minerals by deep learning molecular dynamics simulation: A case of dicalcium silicate

Yunjian Li, Hui Pan, Zongjin Li
Cement and Concrete Research, 2023, 165, 107092.
DOI: 10.1016/j.cemconres.2023.107092

Profiling the off-center atomic displacements in CuCl at finite temperatures with a deep-learning potential

Zhi-Hao Wang, Xuan-Yan Chen, Zhen Zhang, Xie Zhang, Su- Huai Wei
Phys. Rev. Materials, 2023, 7, 34601.
DOI: 10.1103/PhysRevMaterials.7.034601

Liquid-Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems

I. A. Balyakin, R. E. Ryltsev, N. M. Chtchelkatchev
Jetp Lett., 2023, 117, 370–376.
DOI: 10.1134/S0021364023600234

Atomic structure, stability, and dissociation of dislocations in cadmium telluride

Jun Li, Kun Luo, Qi An
International Journal of Plasticity, 2023, 163, 103552.
DOI: 10.1016/j.ijplas.2023.103552

The highest melting point material: Searched by Bayesian global optimization with deep potential molecular dynamics

Yinan Wang, Bo Wen, Xingjian Jiao, Ya Li, Lei Chen, Yujin Wang, Fu-Zhi Dai
2023, 12, 803–814.
DOI: 10.26599/JAC.2023.9220721

Structural and Composition Evolution of Palladium Catalyst for CO Oxidation under Steady-State Reaction Conditions

Jiawei Wu, Dingming Chen, Jianfu Chen, Haifeng Wang
J. Phys. Chem. C, 2023, 127, 6262–6270.
DOI: 10.1021/acs.jpcc.2c07877

Monitoring the melting behavior of boron nanoparticles using a neural network potential

Xiaoya Chang, Qingzhao Chu, Dongping Chen
Phys. Chem. Chem. Phys., 2023, 25, 12841–12853.
DOI: 10.1039/d3cp00571b

Unraveling the Dynamic Correlations between Transition Metal Migration and the Oxygen Dimer Formation in the Highly Delithiated LixCoO2 Cathode

Taiping Hu, Fu-Zhi Dai, Guobing Zhou, Xiaoxu Wang, Shenzhen Xu
J. Phys. Chem. Lett., 2023, 14, 3677–3684.
DOI: 10.1021/acs.jpclett.3c00506

Hydrogen distribution between the Earth's inner and outer core

Liang Yuan, Gerd Steinle-Neumann
Earth and Planetary Science Letters, 2023, 609, 118084.
DOI: 10.1016/j.epsl.2023.118084

Lattice Thermal Conductivity of Monolayer InSe Calculated by Machine Learning Potential

Jinsen Han, Qiyu Zeng, Ke Chen, Xiaoxiang Yu, Jiayu Dai
Nanomaterials (Basel)., 2023, 13, 1576.
DOI: 10.3390/nano13091576

Modeling the high-pressure solid and liquid phases of tin from deep potentials with ab initio accuracy

Tao Chen, Fengbo Yuan, Jianchuan Liu, Huayun Geng, Linfeng Zhang, Han Wang, Mohan Chen
Phys. Rev. Materials, 2023, 7, 53603.
DOI: 10.1103/PhysRevMaterials.7.053603

A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water

Ignacio Sanchez-Burgos, Maria Carolina Muniz, Jorge R Espinosa, Athanassios Z Panagiotopoulos
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0144500

First-Principles-Based Machine Learning Models for Phase Behavior and Transport Properties of CO2

Reha Mathur, Maria Carolina Muniz, Shuwen Yue, Roberto Car, Athanassios Z Panagiotopoulos
J. Phys. Chem. B, 2023, 127, 4562–4569.
DOI: 10.1021/acs.jpcb.3c00610

An accurate interatomic potential for the TiAlNb ternary alloy developed by deep neural network learning method

Jiajun Lu, Jinkai Wang, Kaiwei Wan, Ying Chen, Hao Wang, Xinghua Shi
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0147720

In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol

Siddarth K Achar, Leonardo Bernasconi, Ruby I DeMaio, Katlyn R Howard, J Karl Johnson
ACS Appl. Mater. Interfaces, 2023, 15, 25873–25883.
DOI: 10.1021/acsami.3c04022

Noble gas (He, Ne, and Ar) solubilities in high-pressure silicate melts calculated based on deep-potential modeling

Kai Wang, Xiancai Lu, Xiandong Liu, Kun Yin
Geochimica et Cosmochimica Acta, 2023, 350, 57–68.
DOI: 10.1016/j.gca.2023.03.032

Deciphering the Anomalous Acidic Tendency of Terminal Water at Rutile(110)-Water Interfaces

Yong-Bin Zhuang, Jun Cheng
J. Phys. Chem. C, 2023, 127, 10532–10540.
DOI: 10.1021/acs.jpcc.3c01870

Investigating the Hydroxyl Reorientation in Hydroxyapatite Using Machine Learning Potentials

Jing Wang, Xin Wang, Hua Zhu, Dingguo Xu
J. Phys. Chem. C, 2023, 127, 11369–11377.
DOI: 10.1021/acs.jpcc.3c02426

Molecular insight into the GaP(110)-water interface using machine learning accelerated molecular dynamics

Xue-Ting Fan, Xiao-Jian Wen, Yong-Bin Zhuang, Jun Cheng
Journal of Energy Chemistry, 2023, 82, 239–247.
DOI: 10.1016/j.jechem.2023.03.013

Revealing Carbon Vacancy Distribution on $\alpha$-MoC1-x Surfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach

Jun-Zhong Xie, Hong Jiang
J. Phys. Chem. C, 2023, 127, 13228–13237.
DOI: 10.1021/acs.jpcc.3c01941

Fluorine spillover for ceria- vs silica-supported palladium nanoparticles: A MD study using machine learning potentials

Da-Jiang Liu, James W Evans
J. Chem. Phys., 2023, 159.
DOI: 10.1063/5.0147132

Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab~initio simulations

N M Chtchelkatchev, R E Ryltsev, M V Magnitskaya, S M Gorbunov, K A Cherednichenko, V L Solozhenko, V V Brazhkin
J. Chem. Phys., 2023, 159.
DOI: 10.1063/5.0165948

Deep Potential Molecular Dynamics Study of Chapman-Jouguet Detonation Events of Energetic Materials

Jidong Zhang, Wei Guo, Yugui Yao
J. Phys. Chem. Lett., 2023, 14, 7141–7148.
DOI: 10.1021/acs.jpclett.3c01392

Deep neural network potential for simulating hydrogen blistering in tungsten

Xiao-Yang Wang, Yi-Nan Wang, Ke Xu, Fu-Zhi Dai, Hai-Feng Liu, Guang-Hong Lu, Han Wang
Phys. Rev. Materials, 2023, 7, 93601.
DOI: 10.1103/PhysRevMaterials.7.093601

Unraveling the Atomic-scale Mechanism of Phase Transformations and Structural Evolutions during (de)Lithiation in Si Anodes

Fangjia Fu, Xiaoxu Wang, Linfeng Zhang, Yifang Yang, Jianhui Chen, Bo Xu, Chuying Ouyang, Shenzhen Xu, Fu-Zhi Dai, Weinan E
Adv Funct Materials, 2023, 33.
DOI: 10.1002/adfm.202303936

Collective motion in hcp-Fe at Earth\textquoterights inner core conditions

Youjun Zhang, Yong Wang, Yuqian Huang, Junjie Wang, Zhixin Liang, Long Hao, Zhipeng Gao, Jun Li, Qiang Wu, Hong Zhang, Yun Liu, Jian Sun, Jung-Fu Lin
Proc. Natl. Acad. Sci. U. S. A., 2023, 120, e2309952120.
DOI: 10.1073/pnas.2309952120

Machine learning potential for Ab Initio phase transitions of zirconia

Yuanpeng Deng, Chong Wang, Xiang Xu, Hui Li
Theoretical and Applied Mechanics Letters, 2023, 13, 100481.
DOI: 10.1016/j.taml.2023.100481

Modelling electrified microporous carbon/electrolyte electrochemical interface and unraveling charge storage mechanism by machine learning accelerated molecular dynamics

Yifeng Zhang, Hui Huang, Jie Tian, Chengwei Li, Yuchen Jiang, Zeng Fan, Lujun Pan
Energy Storage Materials, 2023, 63, 103069.
DOI: 10.1016/j.ensm.2023.103069

Data-driven prediction of complex crystal structures of dense lithium

Xiaoyang Wang, Zhenyu Wang, Pengyue Gao, Chengqian Zhang, Jian Lv, Han Wang, Haifeng Liu, Yanchao Wang, Yanming Ma
Nat. Commun., 2023, 14, 2924.
DOI: 10.1038/s41467-023-38650-y

Realizing long-cycling all-solid-state Li-In||TiS2 batteries using Li6+xMxAs1-xS5I (M=Si, Sn) sulfide solid electrolytes

Pushun Lu, Yu Xia, Guochen Sun, Dengxu Wu, Siyuan Wu, Wenlin Yan, Xiang Zhu, Jiaze Lu, Quanhai Niu, Shaochen Shi, Zhengju Sha, Liquan Chen, Hong Li, Fan Wu
Nat. Commun., 2023, 14, 4077.
DOI: 10.1038/s41467-023-39686-w

Dislocation-mediated migration of the $\alpha$/$\beta$ interfaces in titanium

Jin-Yu Zhang, Zhi-Peng Sun, Dong Qiu, Fu-Zhi Dai, Yang- Sheng Zhang, Dongsheng Xu, Wen-Zheng Zhang
Acta Materialia, 2023, 261, 119364.
DOI: 10.1016/j.actamat.2023.119364

Interfacial heat and mass transfer at silica/binary molten salt interface from deep potential molecular dynamics

Fei Liang, Jing Ding, Xiaolan Wei, Gechuanqi Pan, Shule Liu
International Journal of Heat and Mass Transfer, 2023, 217, 124705.
DOI: 10.1016/j.ijheatmasstransfer.2023.124705

Revisiting the structure, interaction, and dynamical property of ionic liquid from the deep learning force field

Yulong Ling, Kun Li, Mi Wang, Junfeng Lu, Chenlu Wang, Yanlei Wang, Hongyan He
Journal of Power Sources, 2023, 555, 232350.
DOI: 10.1016/j.jpowsour.2022.232350

Solvation structures of calcium and magnesium ions in water with the presence of hydroxide: a study by deep potential molecular dynamics

Jianchuan Liu, Renxi Liu, Yu Cao, Mohan Chen
Phys. Chem. Chem. Phys., 2023.
DOI: 10.1039/d2cp04105g

Accurate interatomic potential for the nucleation in liquid Ti-Al binary alloy developed by deep neural network learning method

B. Zhai, H.P. Wang
Computational Materials Science, 2023, 216, 111843.
DOI: 10.1016/j.commatsci.2022.111843

2022

Convergence acceleration in machine learning potentials for atomistic simulations

Dylan Bayerl, Christopher M. Andolina, Shyam Dwaraknath, Wissam A. Saidi
Digital Discovery, 2022, 1, 61–69.
DOI: 10.1039/d1dd00005e

Towards fully ab initio simulation of atmospheric aerosol nucleation

Shuai Jiang, Yi-Rong Liu, Teng Huang, Ya-Juan Feng, Chun- Yu Wang, Zhong-Quan Wang, Bin-Jing Ge, Quan-Sheng Liu, Wei-Ran Guang, Wei Huang
Nat. Commun., 2022, 13, 6067.
DOI: 10.1038/s41467-022-33783-y

Modeling Chemical Reactions in Alkali Carbonate-Hydroxide Electrolytes with Deep Learning Potentials

Anirban Mondal, Dina Kussainova, Shuwen Yue, Athanassios Z Panagiotopoulos
J. Chem. Theory Comput., 2022.
DOI: 10.1021/acs.jctc.2c00816

Lattice Thermal Conductivity of MgSiO3 Perovskite and Post- Perovskite under Lower Mantle Conditions Calculated by Deep Potential Molecular Dynamics

Fenghu Yang, Qiyu Zeng, Bo Chen, Dongdong Kang, Shen Zhang, Jianhua Wu, Xiaoxiang Yu, Jiayu Dai
Chinese Phys. Lett., 2022, 39, 116301.
DOI: 10.1088/0256-307X/39/11/116301

Resolving the odd-even oscillation of water dissociation at rutile TiO2(110)-water interface by machine learning accelerated molecular dynamics

Yong-Bin Zhuang, Rui-Hao Bi, Jun Cheng
J. Chem. Phys., 2022, 157, 164701.
DOI: 10.1063/5.0126333

Origin of negative thermal expansion and pressure-induced amorphization in zirconium tungstate from a machine-learning potential

Ri He, Hongyu Wu, Yi Lu, Zhicheng Zhong
Phys. Rev. B, 2022, 106, 174101.
DOI: 10.1103/PhysRevB.106.174101

Classical and machine learning interatomic potentials for BCC vanadium

Rui Wang, Xiaoxiao Ma, Linfeng Zhang, Han Wang, David J. Srolovitz, Tongqi Wen, Zhaoxuan Wu
Phys. Rev. Materials, 2022, 6, 113603.
DOI: 10.1103/PhysRevMaterials.6.113603

Metal Affinity of Support Dictates Sintering of Gold Catalysts

Jin-Cheng Liu, Langli Luo, Hai Xiao, Junfa Zhu, Yang He, Jun Li
J. Am. Chem. Soc., 2022, 144, 20601–20609.
DOI: 10.1021/jacs.2c06785

Multireference Generalization of the Weighted Thermodynamic Perturbation Method

Timothy J Giese, Jinzhe Zeng, Darrin M York
J. Phys. Chem. A, 2022, 126, 8519–8533.
DOI: 10.1021/acs.jpca.2c06201

Thermal Conductivity of Hydrous Wadsleyite Determined by Non-Equilibrium Molecular Dynamics Based on Machine Learning

Dong Wang, Zhongqing Wu, Xin Deng
Geophysical Research Letters, 2022, 49.
DOI: 10.1029/2022GL100337

Deep potential for a face-centered cubic Cu system at finite temperatures

Yunzhen Du, Zhaocang Meng, Qiang Yan, Canglong Wang, Yuan Tian, Wenshan Duan, Sheng Zhang, Ping Lin
Phys. Chem. Chem. Phys., 2022, 24, 18361–18369.
DOI: 10.1039/D2CP02758E

Structural and electrocatalytic properties of copper clusters: A study via deep learning and first principles

Xiaoning Wang, Haidi Wang, Qiquan Luo, Jinlong Yang
J. Chem. Phys., 2022, 157, 74304.
DOI: 10.1063/5.0100505

High accuracy neural network interatomic potential for NiTi shape memory alloy

Hao Tang, Yin Zhang, Qing-Jie Li, Haowei Xu, Yuchi Wang, Yunzhi Wang, Ju Li
Acta Materialia, 2022, 238, 118217.
DOI: 10.1016/j.actamat.2022.118217

A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment

Xiaoyang Wang, Yinan Wang, Linfeng Zhang, Fuzhi Dai, Han Wang
Nucl. Fusion, 2022, 62, 126013.
DOI: 10.1088/1741-4326/ac888b

Molecular dynamics simulations of LiCl ion pairs in high temperature aqueous solutions by deep learning potential

Wei Zhang, Li Zhou, Bin Yang, Tinggui Yan
Journal of Molecular Liquids, 2022, 367, 120500.
DOI: 10.1016/j.molliq.2022.120500

Combined QM/MM, Machine Learning Path Integral Approach to Compute Free Energy Profiles and Kinetic Isotope Effects in RNA Cleavage Reactions

Timothy J Giese, Jinzhe Zeng, Şölen Ekesan, Darrin M York
J. Chem. Theory Comput., 2022, 18, 4304–4317.
DOI: 10.1021/acs.jctc.2c00151

Machine Learning Accelerates Molten Salt Simulations: Thermal Conductivity of MgCl 2 -NaCl Eutectic

Wenshuo Liang, Guimin Lu, Jianguo Yu
Advcd Theory and Sims, 2022, 2200206.
DOI: 10.1002/adts.202200206

Machine Learning Force Field Aided Cluster Expansion Approach to Configurationally Disordered Materials: Critical Assessment of Training Set Selection and Size Convergence

Jun-Zhong Xie, Xu-Yuan Zhou, Dong Luan, Hong Jiang
J. Chem. Theory Comput., 2022, 18, 3795–3804.
DOI: 10.1021/acs.jctc.2c00017

Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66

Siddarth K Achar, Jacob J Wardzala, Leonardo Bernasconi, Linfeng Zhang, J Karl Johnson
J. Chem. Theory Comput., 2022, 18, 3593–3606.
DOI: 10.1021/acs.jctc.2c00010

Towards large-scale and spatiotemporally resolved diagnosis of electronic density of states by deep learning

Qiyu Zeng, Bo Chen, Xiaoxiang Yu, Shen Zhang, Dongdong Kang, Han Wang, Jiayu Dai
Phys. Rev. B, 2022, 105, 174109.
DOI: 10.1103/PhysRevB.105.174109

Exploring Complex Reaction Networks Using Neural Network-Based Molecular Dynamics Simulation

Qingzhao Chu, Kai H Luo, Dongping Chen
J. Phys. Chem. Lett., 2022, 13, 4052–4057.
DOI: 10.1021/acs.jpclett.2c00647

Acids at the Edge: Why Nitric and Formic Acid Dissociations at Air-Water Interfaces Depend on Depth and on Interface Specific Area

Miguel de la Puente, Rolf David, Axel Gomez, Damien Laage
J. Am. Chem. Soc., 2022, 144, 10524–10529.
DOI: 10.1021/jacs.2c03099

Dissolving salt is not equivalent to applying a pressure on water

Chunyi Zhang, Shuwen Yue, Athanassios Z Panagiotopoulos, Michael L Klein, Xifan Wu
Nat. Commun., 2022, 13, 822.
DOI: 10.1038/s41467-022-28538-8

Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

Paolo Pegolo, Stefano Baroni, Federico Grasselli
npj Comput Mater, 2022, 8, 24.
DOI: 10.1038/s41524-021-00693-4

The chemical origin of temperature-dependent lithium-ion concerted diffusion in sulfide solid electrolyte Li10GeP2S12

Zhong-Heng Fu, Xiang Chen, Nan Yao, Xin Shen, Xia-Xia Ma, Shuai Feng, Shuhao Wang, Rui Zhang, Linfeng Zhang, Qiang Zhang
Journal of Energy Chemistry, 2022, 70, 59–66.
DOI: 10.1016/j.jechem.2022.01.018

Efficient and accurate atomistic modeling of dopant migration using deep neural network

Xi Ding, Ming Tao, Junhua Li, Mingyuan Li, Mengchao Shi, Jiashu Chen, Zhen Tang, Francis Benistant, Jie Liu
Materials Science in Semiconductor Processing, 2022, 143, 106513.
DOI: 10.1016/j.mssp.2022.106513

Exploring the Effects of Ionic Defects on the Stability of CsPbI 3 with a Deep Learning Potential

Weijie Yang, Jiajia Li, Xuelu Chen, Yajun Feng, Chongchong Wu, Ian D Gates, Zhengyang Gao, Xunlei Ding, Jianxi Yao, Hao Li
Chemphyschem, 2022, 23, e202100841.
DOI: 10.1002/cphc.202100841

Self-Healing Mechanism of Lithium in Lithium Metal

Junyu Jiao, Genming Lai, Liang Zhao, Jiaze Lu, Qidong Li, Xianqi Xu, Yao Jiang, Yan-Bing He, Chuying Ouyang, Feng Pan, Hong Li, Jiaxin Zheng
Adv. Sci. (Weinh)., 2022, 9, e2105574.
DOI: 10.1002/advs.202105574

A deep potential model with long-range electrostatic interactions

Linfeng Zhang, Han Wang, Maria Carolina Muniz, Athanassios Z Panagiotopoulos, Roberto Car, Weinan E
J. Chem. Phys., 2022, 156, 124107.
DOI: 10.1063/5.0083669

A generalizable machine learning potential of Ag-Au nanoalloys and its application to surface reconstruction, segregation and diffusion

YiNan Wang, LinFeng Zhang, Ben Xu, XiaoYang Wang, Han Wang
Modelling Simul. Mater. Sci. Eng., 2022, 30, 25003.
DOI: 10.1088/1361-651X/ac4002

Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water

Manyi Yang, Luigi Bonati, Daniela Polino, Michele Parrinello
Catalysis Today, 2022, 387, 143–149.
DOI: 10.1016/j.cattod.2021.03.018

Molecular dynamics simulation of molten strontium chloride based on deep potential

Di Guo, Jia Zhao, Wenshuo Liang, Guimin Lu
Journal of Molecular Liquids, 2022, 348, 118380.
DOI: 10.1016/j.molliq.2021.118380

Structural phase transitions in $\mathrmSrTi\mathrmO_3$ from deep potential molecular dynamics

Ri He, Hongyu Wu, Linfeng Zhang, Xiaoxu Wang, Fangjia Fu, Shi Liu, Zhicheng Zhong
Phys. Rev. B, 2022, 105, 064104.
DOI: 10.1103/PhysRevB.105.064104

A deep learning potential applied in tobermorite phases and extended to calcium silicate hydrates

Yang Zhou, Haojie Zheng, Weihuan Li, Tao Ma, Changwen Miao
Cement and Concrete Research, 2022, 152, 106685.
DOI: 10.1016/j.cemconres.2021.106685

2021

Insights from Computational Studies on the Anisotropic Volume Change of LixNiO2 at High States of Charge (x < 0.25)

Juan C. Garcia, Joshua Gabriel, Noah H. Paulson, John Low, Marius Stan, Hakim Iddir
J. Phys. Chem. C, 2021, 125 (49), 27130-27139.
DOI: 10.1021/acs.jpcc.1c08022

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

Tongqi Wen, Rui Wang, Lingyu Zhu, Linfeng Zhang, Han Wang, David J. Srolovitz, Zhaoxuan Wu
npj Comput Mater, 2021, 7, 206.
DOI: 10.1038/s41524-021-00661-y

Artificial intelligence model for efficient simulation of monatomic phase change material antimony

Mengchao Shi, Junhua Li, Ming Tao, Xin Zhang, Jie Liu
Materials Science in Semiconductor Processing, 2021, 136, 106146.
DOI: 10.1016/j.mssp.2021.106146

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution

Jinzhe Zeng, Timothy J Giese, Şölen Ekesan, Darrin M York
J. Chem. Theory Comput., 2021, 17, 6993–7009.
DOI: 10.1021/acs.jctc.1c00201

Accurate force field of two-dimensional ferroelectrics from deep learning

Jing Wu, Liyi Bai, Jiawei Huang, Liyang Ma, Jian Liu, Shi Liu
Phys. Rev. B, 2021, 104, 174107.
DOI: 10.1103/PhysRevB.104.174107

Liquid-Liquid Critical Point in Phosphorus

Manyi Yang, Tarak Karmakar, Michele Parrinello
Phys. Rev. Lett., 2021, 127, 80603.
DOI: 10.1103/PhysRevLett.127.080603

Anomalous Behavior of Viscosity and Electrical Conductivity of MgSiO 3 Melt at Mantle Conditions

Haiyang Luo, Bijaya B. Karki, Dipta B. Ghosh, Huiming Bao
Geophys Res Lett, 2021, 48.
DOI: 10.1029/2021GL093573

Phase Diagram of a Deep Potential Water Model

Linfeng Zhang, Han Wang, Roberto Car, Weinan E
Phys. Rev. Lett., 2021, 126, 236001.
DOI: 10.1103/PhysRevLett.126.236001

The thermoelectric performance of new structure SnSe studied by quotient graph and deep learning potential

D. Guo, C. Li, K. Li, B. Shao, D. Chen, Y. Ma, J. Sun, X. Cao, W. Zeng, X. Chang
Materials Today Energy, 2021, 20, 100665.
DOI: 10.1016/j.mtener.2021.100665

Phase Equilibrium of Water with Hexagonal and Cubic Ice Using the SCAN Functional

Pablo M Piaggi, Athanassios Z Panagiotopoulos, Pablo G Debenedetti, Roberto Car
J. Chem. Theory Comput., 2021, 17, 3065–3077.
DOI: 10.1021/acs.jctc.1c00041

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

Wanrun Jiang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Chinese Phys. B, 2021, 30, 50706.
DOI: 10.1088/1674-1056/abf134

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

Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng, Weinan E
J. Chem. Phys., 2021, 154, 94703.
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Anharmonic Raman spectra simulation of crystals from deep neural networks

Honghui Shang, Haidi Wang
AIP Advances, 2021, 11, 35105.
DOI: 10.1063/5.0040190

Deep learning of accurate force field of ferroelectric HfO2

Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Phys. Rev. B, 2021, 103, 24108.
DOI: 10.1103/PhysRevB.103.024108

Theoretical study of Na+ transport in the solid-state electrolyte Na3OBr based on deep potential molecular dynamics

Han-Xiao Li, Xu-Yuan Zhou, Yue-Chao Wang, Hong Jiang
Inorg. Chem. Front., 2021, 8, 425–432.
DOI: 10.1039/D0QI00921K

Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator

Jinzhe Zeng, Linfeng Zhang, Han Wang, Tong Zhu
Energy & Fuels, 2021, 35 (1), 762–769.
DOI: 10.1021/acs.energyfuels.0c03211

Diffusional fractionation of helium isotopes in silicate melts

H. Luo, B.B. Karki, D.B. Ghosh, H. Bao
Geochem. Persp. Let., 2021, 19–22.
DOI: 10.7185/geochemlet.2128

Deep-learning potential method to simulate shear viscosity of liquid aluminum at high temperature and high pressure by molecular dynamics

Yuqing Cheng, Han Wang, Shuaichuang Wang, Xingyu Gao, Qiong Li, Jun Fang, Hongzhou Song, Weidong Chu, Gongmu Zhang, Haifeng Song, Haifeng Liu
AIP Advances, 2021, 11, 15043.
DOI: 10.1063/5.0036298

2020

Crystal Structure Prediction of Binary Alloys via Deep Potential

Haidi Wang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Front. Chem., 2020, 8, 589795.
DOI: 10.3389/fchem.2020.589795

Signatures of a liquid-liquid transition in an ab initio deep neural network model for water

Thomas E Gartner 3rd, Linfeng Zhang, Pablo M Piaggi, Roberto Car, Athanassios Z Panagiotopoulos, Pablo G Debenedetti
Proc. Natl. Acad. Sci. U. S. A., 2020, 117, 26040–26046.
DOI: 10.1073/pnas.2015440117

A Deep-Learning Potential for Crystalline and Amorphous Li-Si Alloys

Nan Xu, Yao Shi, Yi He, Qing Shao
J. Phys. Chem. C, 2020, 124, 16278–16288.
DOI: 10.1021/acs.jpcc.0c03333

Deep neural network for the dielectric response of insulators

Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, Roberto Car
Phys. Rev. B, 2020, 102, 41121.
DOI: 10.1103/PhysRevB.102.041121

Raman spectrum and polarizability of liquid water from deep neural networks

Grace M Sommers, Marcos F Calegari Andrade, Linfeng Zhang, Han Wang, Roberto Car
Phys. Chem. Chem. Phys., 2020, 22, 10592–10602.
DOI: 10.1039/D0CP01893G

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

Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E
Computer Physics Communications, 2020, 253, 107206.
DOI: 10.1016/j.cpc.2020.107206

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