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Publications driven by DeePMD-kit

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

2023

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

Centroid Molecular Dynamics Can Be Greatly Accelerated through Neural Network Learned Centroid Forces Derived from Path Integral Molecular Dynamics

Timothy D Loose, Patrick G Sahrmann, Gregory A Voth
J. Chem. Theory Comput., 2022, 18, 5856–5863.
DOI: 10.1021/acs.jctc.2c00706

Moir'e Phonons in Magic-Angle Twisted Bilayer Graphene

Xiaoqian Liu, Ran Peng, Zhaoru Sun, Jianpeng Liu
Nano Lett., 2022, 22, 7791–7797.
DOI: 10.1021/acs.nanolett.2c02010

Modeling the Solvation and Acidity of Carboxylic Acids Using an Ab Initio Deep Neural Network Potential

Abhinav S Raman, Annabella Selloni
J. Phys. Chem. A, 2022, 126, 7283–7290.
DOI: 10.1021/acs.jpca.2c06252

Photoelectron spectra of water and simple aqueous solutions at extreme conditions

Zifan Ye, Cunzhi Zhang, Giulia Galli
Faraday Discuss., 2022, 236, 352–363.
DOI: 10.1039/d2fd00003b

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

Thermal transport properties of monolayer GeS and SnS: A comparative study based on machine learning and SW interatomic potential models

Wentao Li, Chenxiu Yang
AIP Advances, 2022, 12, 85111.
DOI: 10.1063/5.0099448

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

A Deep Neural Network Interface Potential for Li-Cu Systems

Genming Lai, Junyu Jiao, Chi Fang, Ruiqi Zhang, Xianqi Xu, Liyuan Sheng, Yao Jiang, Chuying Ouyang, Jiaxin Zheng
Adv Materials Inter, 2022, 9, 2201346.
DOI: 10.1002/admi.202201346

Strategy to consider element distribution when constructing training datasets for developing machine learning potentials of alloys based on a Monte-Carlo-like method

Zhipeng Zhang, Liuqing Chen, Junyi Guo, Xianyin Duan, Bin Shan, Xianbao Duan
Phys. Rev. B, 2022, 106, 94107.
DOI: 10.1103/PhysRevB.106.094107

Combining Machine Learning Approaches and Accurate Ab Initio Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution

Timoth'ee Devergne, Th'eo Magrino, Fabio Pietrucci, A Marco Saitta
J. Chem. Theory Comput., 2022, 18, 5410–5421.
DOI: 10.1021/acs.jctc.2c00400

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

Zheyong Fan, Yanzhou Wang, Penghua Ying, Keke Song, Junjie Wang, Yong Wang, Zezhu Zeng, Ke Xu, Eric Lindgren, J Magnus Rahm, Alexander J Gabourie, Jiahui Liu, Haikuan Dong, Jianyang Wu, Yue Chen, Zheng Zhong, Jian Sun, Paul Erhart, Yanjing Su, Tapio Ala-Nissila
J. Chem. Phys., 2022, 157, 114801.
DOI: 10.1063/5.0106617

Magnetocaloric effect in ScGdTbDyHo high-entropy alloy: Impact of synthesis route

S.A. Uporov, S. Kh Estemirova, E.V. Sterkhov, I.A. Balyakin, A.A. Rempel
Intermetallics, 2022, 151, 107678.
DOI: 10.1016/j.intermet.2022.107678

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

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

Denghui Lu, Wanrun Jiang, Yixiao Chen, Linfeng Zhang, Weile Jia, Han Wang, Mohan Chen
18, 2022, 5555–5567.
DOI: 10.1021/acs.jctc.2c00102

Combining Machine Learning Approaches and Accurate Ab Initio Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution

Timoth'ee Devergne, Th'eo Magrino, Fabio Pietrucci, A Marco Saitta
J. Chem. Theory Comput., 2022.
DOI: 10.1021/acs.jctc.2c00400

A Deep Neural Network Interface Potential for Li-Cu Systems

Genming Lai, Junyu Jiao, Chi Fang, Ruiqi Zhang, Xianqi Xu, Liyuan Sheng, Yao Jiang, Chuying Ouyang, Jiaxin Zheng
Adv Materials Inter, 2022, 2201346.
DOI: 10.1002/admi.202201346

Nucleation of Water Clusters in Gas Phase: A Computational Study Based on Neural Network Potential and Enhanced Sampling\textreferencemark

Sen Xu, Liling Wu, Zhenyu Li
Acta Chimica Sinica, 2022, 80, 598.
DOI: 10.6023/A22010003

Toward High-level Machine Learning Potential for Water Based on Quantum Fragmentation and Neural Networks

Jinfeng Liu, Jinggang Lan, Xiao He
J. Phys. Chem. A, 2022, 126, 3926–3936.
DOI: 10.1021/acs.jpca.2c00601

A Deep Neural Network Potential for Water Confined in Graphene Nanocapillaries

Wen Zhao, Hu Qiu, Wanlin Guo
J. Phys. Chem. C, 2022, 126, 10546–10553.
DOI: 10.1021/acs.jpcc.2c02423

Soft-phonon anharmonicity, floppy modes, and Na diffusion in Na3FY (Y=S,Se,Te): Ab initio and machine-learned molecular dynamics simulations

Mayanak Kumar Gupta, Sajan Kumar, Ranjan Mittal, Samrath L. Chaplot
Phys. Rev. B, 2022, 106, 14311.
DOI: 10.1103/PhysRevB.106.014311

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

Automated workflow for computation of redox potentials, acidity constants, and solvation free energies accelerated by machine learning

Feng Wang, Jun Cheng
J. Chem. Phys., 2022, 157, 24103.
DOI: 10.1063/5.0098330

Homogeneous ice nucleation in an ab initio machine-learning model of water

Pablo M Piaggi, Jack Weis, Athanassios Z Panagiotopoulos, Pablo G Debenedetti, Roberto Car
Proc. Natl. Acad. Sci. U. S. A., 2022, 119, e2207294119.
DOI: 10.1073/pnas.2207294119

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

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

Deep neural network based quantum simulations and quasichemical theory for accurate modeling of molten salt thermodynamics

Yu Shi, Stephen T. Lam, Thomas~L. Beck
Chem. Sci., 2022.
DOI: 10.1039/D2SC02227C

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

Reaction processes at step edges on S-decorated Cu(111) and Ag(111) surfaces: MD analysis utilizing machine learning derived potentials

Da-Jiang Liu, James W Evans
J. Chem. Phys., 2022, 156, 204106.
DOI: 10.1063/5.0089210

Deep machine learning potential for atomistic simulation of Fe-Si-O systems under Earth's outer core conditions

Chao Zhang, Ling Tang, Yang Sun, Kai-Ming Ho, Renata M. Wentzcovitch, Cai-Zhuang Wang
Phys. Rev. Materials, 2022, 6, 63802.
DOI: 10.1103/PhysRevMaterials.6.063802

Accelerated Deep Learning Dynamics for Atomic Layer Deposition of Al(Me)3 and Water on OH/Si(111)

Hiroya Nakata, Michael Filatov Gulak, Cheol Ho Choi
ACS Appl. Mater. Interfaces, 2022, 14, 26116–26127.
DOI: 10.1021/acsami.2c01768

Acids at the Edge: Why Nitric and Formic Acid Dissociations at Air\textendashWater 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

Strongly Anharmonic Phonons and Their Role in Superionic Diffusion and Ultralow Thermal Conductivity of Cu 7 PSe 6

Mayanak K. Gupta, Jingxuan Ding, Dipanshu Bansal, Douglas L. Abernathy, Georg Ehlers, Naresh C. Osti, Wolfgang G. Zeier, Olivier Delaire
Advanced Energy Materials, 2022, 12, 2200596.
DOI: 10.1002/aenm.202200596

Atomistic Calculation of the Melting Point of the High-Entropy Cantor Alloy CoCrFeMnNi

I. A. Balyakin, A. A. Rempel
Dokl Phys Chem, 2022, 502, 11–17.
DOI: 10.1134/S0012501622010018

Deep potential development of transition-metal-rich carbides

Tyler McGilvry-James, Bikash Timalsina, Marium Mostafiz Mou, Ridwan Sakidja
MRS Advances, 2022, 7, 468–473.
DOI: 10.1557/s43580-022-00289-0

Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture

Pinghui Mo, Chang Li, Dan Zhao, Yujia Zhang, Mengchao Shi, Junhua Li, Jie Liu
npj Comput Mater, 2022, 8, 107.
DOI: 10.1038/s41524-022-00773-z

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

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

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

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

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 learning interatomic potential developed for atomistic simulation of carbon materials

Jinjin Wang, Hong Shen, Riyi Yang, Kun Xie, Chao Zhang, Liangyao Chen, Kai-Ming Ho, Cai-Zhuang Wang, Songyou Wang
Carbon, 2022, 186, 1–8.
DOI: 10.1016/j.carbon.2021.09.062

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

Development of neural network potential for MD simulation and its application to TiN

Takeru Miyagawa, Kazuki Mori, Nobuhiko Kato, Akio Yonezu
Computational Materials Science, 2022, 206, 111303.
DOI: 10.1016/j.commatsci.2022.111303

Ab Initio Neural Network MD Simulation of Thermal Decomposition of High Energy Material CL-20/TNT

Liqun Cao, Jinzhe Zeng, Bo Wang, Tong Zhu, John Z.H. Zhang
Phys. Chem. Chem. Phys., 2022, 24, 11801–11811.
DOI: 10.1039/D2CP00710J

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

Grain boundary segregation induced strong UHTCs at elevated temperatures: A universal mechanism from conventional UHTCs to high entropy UHTCs

Fu-Zhi Dai, Bo Wen, Yinjie Sun, Yixiao Ren, Huimin Xiang, Yanchun Zhou
Journal of Materials Science & Technology, 2022, 123, 26-33.
DOI: 10.1016/j.jmst.2021.12.074

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

Study on the structural properties of refining slags by molecular dynamics with deep learning potential

Yuhan Sun, Min Tan, Tao Li, Junguo Li, Bo Shang
Journal of Molecular Liquids, 2022, 353, 118787.
DOI: 10.1016/j.molliq.2022.118787

Nanotwinning induced decreased lattice thermal conductivity of high temperature thermoelectric boron subphosphide (B12P2) from deep learning potential simulations

Xiaona Huang, Yidi Shen, Qi An
Energy and AI, 2022, 8, 100135.
DOI: 10.1016/j.egyai.2022.100135

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

Four-Phonon Scattering Effect and Two-Channel Thermal Transport in Two-Dimensional Paraelectric SnSe

Jie Sun, Cunzhi Zhang, Zhonghua Yang, Yiheng Shen, Ming Hu, Qian Wang
ACS Appl. Mater. Interfaces, 2022, 14, 11493–11499.
DOI: 10.1021/acsami.1c24488

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds

Yanqiang Han, Zhilong Wang, An Chen, Imran Ali, Junfei Cai, Simin Ye, Jinjin Li
Brief. Bioinform., 2022, 23.
DOI: 10.1093/bib/bbab590

Sulfur-enhanced dynamics of coinage metal(111) surfaces: Step edges versus terraces as locations for metal-sulfur complex formation

Da-Jiang Liu, James W. Evans
Journal of Vacuum Science \& Technology A, 2022, 40 (2), 023205.
DOI: 10.1116/6.0001408

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

Deep machine learning potentials for multicomponent metallic melts: Development, predictability and compositional transferability

R.E. Ryltsev, N.M. Chtchelkatchev
Journal of Molecular Liquids, 2022, 349, 118181.
DOI: 10.1016/j.molliq.2021.118181

Neural network potential for Zr-Rh system by machine learning

Kun Xie, Chong Qiao, Hong Shen, Riyi Yang, Ming Xu, Chao Zhang, Yuxiang Zheng, Rongjun Zhang, Liangyao Chen, Kai-Ming Ho, Cai-Zhuang Wang, Songyou Wang
J. Phys. Condens. Matter, 2022, 34, 75402.
DOI: 10.1088/1361-648X/ac37dc

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

Suppression of Rayleigh Scattering in Silica Glass by Codoping Boron and Fluorine: Molecular Dynamics Simulations with Force-Matching and Neural Network Potentials

Shingo Urata, Nobuhiro Nakamura, Tomofumi Tada, Aik Rui Tan, Rafael Gómez-Bombarelli, Hideo Hosono
J. Phys. Chem. C, 2022, 126 (4), 2264-2275.
DOI: 10.1021/acs.jpcc.1c10300

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

Deep learning potential for superionic phase of Ag2S

I.A. Balyakin, S.I. Sadovnikov
Computational Materials Science, 2022, 202, 110963.
DOI: 10.1016/j.commatsci.2021.110963

Neural network representation of electronic structure from ab initio molecular dynamics

Qiangqiang Gu, Linfeng Zhang, Ji Feng
Science Bulletin, 2022, 67, 29–37.
DOI: 10.1016/j.scib.2021.09.010

2021

Efficiently Trained Deep Learning Potential for Graphane

Siddarth K. Achar, Linfeng Zhang, J. Karl Johnson
J. Phys. Chem. C, 2021, 125, 14874–14882.
DOI: 10.1021/acs.jpcc.1c01411

2D Heterostructure of Amorphous CoFeB Coating Black Phosphorus Nanosheets with Optimal Oxygen Intermediate Absorption for Improved Electrocatalytic Water Oxidation

Huayu Chen, Junxiang Chen, Pei Ning, Xin Chen, Junhui Liang, Xin Yao, Da Chen, Laishun Qin, Yuexiang Huang, Zhenhai Wen
ACS Nano, 2021, 15, 12418–12428.
DOI: 10.1021/acsnano.1c04715

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.
DOI: 10.1063/5.0041849

Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions

Xiaoliang Pan, Junjie Yang, Richard Van, Evgeny Epifanovsky, Junming Ho, Jing Huang, Jingzhi Pu, Ye Mei, Kwangho Nam, Yihan Shao
J. Chem. Theory Comput., 2021, 17, 5745–5758.
DOI: 10.1021/acs.jctc.1c00565

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

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

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

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

Reactive uptake of N2O5 by atmospheric aerosol is dominated by interfacial processes

Mirza Galib, David T Limmer
Science, 2021, 371, 921–925.
DOI: 10.1126/science.abd7716

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

Denghui Lu, Han Wang, Mohan Chen, Lin Lin, Roberto Car, Weinan E, Weile Jia, Linfeng Zhang
Computer Physics Communications, 2021, 259, 107624.
DOI: 10.1016/j.cpc.2020.107624

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

Thermodynamic and Transport Properties of LiF and FLiBe Molten Salts with Deep Learning Potentials

Alejandro Rodriguez, Stephen Lam, Ming Hu
ACS Appl. Mater. Interfaces, 2021, 13, 55367–55379.
DOI: 10.1021/acsami.1c17942

Heat transport in liquid water from first-principles and deep neural network simulations

Davide Tisi, Linfeng Zhang, Riccardo Bertossa, Han Wang, Roberto Car, Stefano Baroni
Phys. Rev. B, 2021, 104, 224202.
DOI: 10.1103/PhysRevB.104.224202

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

Fast Na diffusion and anharmonic phonon dynamics in superionic Na3PS4

Mayanak K. Gupta, Jingxuan Ding, Naresh C. Osti, Douglas L. Abernathy, William Arnold, Hui Wang, Zachary Hood, Olivier Delaire
Energy Environ. Sci., 2021, 14, 6554-6563.
DOI: 10.1039/D1EE01509E

Experimental observation of localized interfacial phonon modes

Zhe Cheng, Ruiyang Li, Xingxu Yan, Glenn Jernigan, Jingjing Shi, Michael E Liao, Nicholas J Hines, Chaitanya A Gadre, Juan Carlos Idrobo, Eungkyu Lee, Karl D Hobart, Mark S Goorsky, Xiaoqing Pan, Tengfei Luo, Samuel Graham
Nat. Commun., 2021, 12, 6901.
DOI: 10.1038/s41467-021-27250-3

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

Molecular dynamics simulation of metallic Al-Ce liquids using a neural network machine learning interatomic potential

L Tang, K M Ho, C Z Wang
J. Chem. Phys., 2021, 155, 194503.
DOI: 10.1063/5.0066061

Choosing the right molecular machine learning potential

Max Pinheiro Jr, Fuchun Ge, Nicolas Ferr'e, Pavlo O Dral, Mario Barbatti
Chem. Sci., 2021, 12, 14396–14413.
DOI: 10.1039/d1sc03564a

Atomic structure of liquid refractory Nb5Si3 intermetallic compound alloy based upon deep neural network potential

Q. Wang, B. Zhai, H. P. Wang, B. Wei
Journal of Applied Physics, 2021, 130, 185103.
DOI: 10.1063/5.0067157

Azo(xy) vs Aniline Selectivity in Catalytic Nitroarene Reduction by Intermetallics: Experiments and Simulations

Carena L. Daniels, Da-Jiang Liu, Marquix A. S. Adamson, Megan Knobeloch, Javier Vela
J. Phys. Chem. C, 2021, 125 (44), 24440-24450.
DOI: 10.1021/acs.jpcc.1c08569

Resolving the Structural Debate for the Hydrated Excess Proton in Water

Paul B Calio, Chenghan Li, Gregory A Voth
J. Am. Chem. Soc., 2021, 143, 18672–18683.
DOI: 10.1021/jacs.1c08552

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

Gold Segregation Improves Electrocatalytic Activity of Icosahedron Au@Pt Nanocluster: Insights from Machine Learning

Dingming Chen, Zhuangzhuang Lai, Jiawei Zhang, Jianfu Chen, Peijun Hu, Haifeng Wang
Chin. J. Chem., 2021, 39, 3029–3036.
DOI: 10.1002/cjoc.202100352

Condensed Phase Water Molecular Multipole Moments from Deep Neural Network Models Trained on Ab Initio Simulation Data

Yu Shi, Carrie C Doyle, Thomas L Beck
J. Phys. Chem. Lett., 2021, 12, 10310–10317.
DOI: 10.1021/acs.jpclett.1c02328

Learning intermolecular forces at liquid-vapor interfaces

Samuel P Niblett, Mirza Galib, David T Limmer
J. Chem. Phys., 2021, 155, 164101.
DOI: 10.1063/5.0067565

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

Chunyi Zhang, Fujie Tang, Mohan Chen, Jianhang Xu, Linfeng Zhang, Diana Y Qiu, John P Perdew, Michael L Klein, Xifan Wu
J. Phys. Chem. B, 2021, 125, 11444–11456.
DOI: 10.1021/acs.jpcb.1c03884

Deep Density: Circumventing the Kohn-Sham equations via symmetry preserving neural networks

Leonardo Zepeda-N'u\~nez, Yixiao Chen, Jiefu Zhang, Weile Jia, Linfeng Zhang, Lin Lin
Journal of Computational Physics, 2021, 443, 110523.
DOI: 10.1016/j.jcp.2021.110523

First-principles materials simulation and design for alkali and alkaline metal ion batteries accelerated by machine learning

Lujie Jin, Yujin Ji, Hongshuai Wang, Lifeng Ding, Youyong Li
Phys. Chem. Chem. Phys., 2021, 23, 21470–21483.
DOI: 10.1039/d1cp02963k

Local structure elucidation and properties prediction on KCl-CaCl2 molten salt: A deep potential molecular dynamics study

Min Bu, Wenshuo Liang, Guimin Lu, Jianguo Yu
Solar Energy Materials and Solar Cells, 2021, 232, 111346.
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Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

Alberto Torres, Luana S Pedroza, Marivi Fernandez-Serra, Alexandre R Rocha
J. Phys. Chem. B, 2021, 125, 10772–10778.
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Thermal Conductivity of Silicate Liquid Determined by Machine Learning Potentials

Jie Deng, Lars Stixrude
Geophys Res Lett, 2021, 48, e2021GL093806.
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Ab initio validation on the connection between atomistic and hydrodynamic description to unravel the ion dynamics of warm dense matter

Qiyu Zeng, Xiaoxiang Yu, Yunpeng Yao, Tianyu Gao, Bo Chen, Shen Zhang, Dongdong Kang, Han Wang, Jiayu Dai
Phys. Rev. Research, 2021, 3, 33116.
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Liquid-Liquid Critical Point in Phosphorus

Manyi Yang, Tarak Karmakar, Michele Parrinello
Phys. Rev. Lett., 2021, 127, 80603.
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Robust, Multi-Length-Scale, Machine Learning Potential for Ag–Au Bimetallic Alloys from Clusters to Bulk Materials

Christopher M. Andolina, Marta Bon, Daniele Passerone, Wissam A. Saidi
J. Phys. Chem. C, 2021, 125 (31), 17438-17447.
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Improved Al-Mg alloy surface segregation predictions with a machine learning atomistic potential

Md Sabbir Akhanda, S Emad Rezaei, Keivan Esfarjani, Sergiy Krylyuk, Albert V Davydov, Mona Zebarjadi
Phys. Rev. Mater., 2021, 5, 83804.
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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.
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Deep neural network potentials for diffusional lithium isotope fractionation in silicate melts

Haiyang Luo, Bijaya B. Karki, Dipta B. Ghosh, Huiming Bao
Geochimica et Cosmochimica Acta, 2021, 303, 38–50.
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Phase Diagram of a Deep Potential Water Model

Linfeng Zhang, Han Wang, Roberto Car, Weinan E
Phys. Rev. Lett., 2021, 126, 236001.
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Theoretical prediction on the local structure and transport properties of molten alkali chlorides by deep potentials

Wenshuo Liang, Guimin Lu, Jianguo Yu
Journal of Materials Science & Technology, 2021, 75, 78-85.
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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.
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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.
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Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential

Fu-Zhi Dai, Yinjie Sun, Bo Wen, Huimin Xiang, Yanchun Zhou
Journal of Materials Science & Technology, 2021, 72, 8-15.
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Theoretical prediction on the redox potentials of rare-earth ions by deep potentials

Jia Zhao, Wenshuo Liang, Guimin Lu
Ionics, 2021, 27, 2079–2088.
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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.
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Anharmonic Raman spectra simulation of crystals from deep neural networks

Honghui Shang, Haidi Wang
AIP Advances, 2021, 11, 35105.
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Thermal transport by electrons and ions in warm dense aluminum: A combined density functional theory and deep potential study

Qianrui Liu, Junyi Li, Mohan Chen
Matter and Radiation at Extremes, 2021, 6 (2), 026902.
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Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential

Chao Zhang, Yang Sun, Hai-Di Wang, Feng Zhang, Tong-Qi Wen, Kai-Ming Ho, Cai-Zhuang Wang
J. Phys. Chem. C, 2021, 125 (5), 3127-3133.
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Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations

Pablo M. Piaggi, Roberto Car
Molecular Physics, 2021, 119.
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Static and Dynamic Correlations in Water: Comparison of Classical Ab Initio Molecular Dynamics at Elevated Temperature with Path Integral Simulations at Ambient Temperature

Chenghan Li, Francesco Paesani, Gregory A Voth
J. Chem. Theory Comput., 2022, 18, 2124–2131.
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Molecular dynamics simulations of lanthanum chloride by deep learning potential

Taixi Feng, Jia Zhao, Wenshuo Liang, Guimin Lu
Computational Materials Science, 2021, 111014.
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Diffusional fractionation of helium isotopes in silicate melts

H. Luo, B.B. Karki, D.B. Ghosh, H. Bao
Geochem. Persp. Let., 2021, 19–22.
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Short- and medium-range orders in Al90Tb10 glass and their relation to the structures of competing crystalline phases

L. Tang, Z.J. Yang, T.Q. Wen, K.M. Ho, M.J. Kramer, C.Z. Wang
Acta Materialia, 2021, 204, 116513.
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A DFT accurate machine learning description of molten ZnCl2 and its mixtures: 2. Potential development and properties prediction of ZnCl2-NaCl-KCl ternary salt for CSP

Gechuanqi Pan, Jing Ding, Yunfei Du, Duu-Jong Lee, Yutong Lu
Computational Materials Science, 2021, 187, 110055.
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Deep learning of accurate force field of ferroelectricHfO2

Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Phys. Rev. B, 2021, 103, 24108.
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Machine-Learning-Driven Simulations on Microstructure and Thermophysical Properties of MgCl2-KCl Eutectic

Wenshuo Liang, Guimin Lu, Jianguo Yu
ACS Appl. Mater. Interfaces, 2021, 13, 4034–4042.
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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.
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When do short-range atomistic machine-learning models fall short?

Shuwen Yue, Maria Carolina Muniz, Marcos F Calegari Andrade, Linfeng Zhang, Roberto Car, Athanassios Z Panagiotopoulos
J. Chem. Phys., 2021, 154, 34111.
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2020

Free energy of proton transfer at the water-TiO2 interface from ab initio deep potential molecular dynamics

Marcos F Calegari Andrade, Hsin-Yu Ko, Linfeng Zhang, Roberto Car, Annabella Selloni
Chem. Sci., 2020, 11, 2335–2341.
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Theoretical prediction on thermal and mechanical properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by deep learning potential

Fu-Zhi Dai, Bo Wen, Yinjie Sun, Huimin Xiang, Yanchun Zhou
Journal of Materials Science & Technology, 2020, 43, 168–174.
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A deep neural network interatomic potential for studying thermal conductivity of $\beta$-Ga2O3

Ruiyang Li, Zeyu Liu, Andrew Rohskopf, Kiarash Gordiz, Asegun Henry, Eungkyu Lee, Tengfei Luo
Appl. Phys. Lett., 2020, 117, 152102.
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Structure and dynamics of warm dense aluminum: a molecular dynamics study with density functional theory and deep potential

Qianrui Liu, Denghui Lu, Mohan Chen
J. Phys. Condens. Matter, 2020, 32, 144002.
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Ab initio phase diagram and nucleation of gallium

Haiyang Niu, Luigi Bonati, Pablo M Piaggi, Michele Parrinello
Nat. Commun., 2020, 11, 2654.
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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.
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A machine learning based deep potential for seeking the low-lying candidates of Al clusters

P Tuo, X B Ye, B C Pan
J. Chem. Phys., 2020, 152, 114105.
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Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved

Shu Wang, Zhan Ma, Wenxiao Pan
Soft Matter, 2020, 16, 8330–8344.
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Complex reaction processes in combustion unraveled by neural network- based molecular dynamics simulation

Jinzhe Zeng, Liqun Cao, Mingyuan Xu, Tong Zhu, John Z H Zhang
Nat. Commun., 2020, 11, 5713.
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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.
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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.
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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

Jianhang Xu, Chunyi Zhang, Linfeng Zhang, Mohan Chen, Biswajit Santra, Xifan Wu
Phys. Rev. B, 2020, 102, 214113.
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Hydrogen Dynamics in Supercritical Water Probed by Neutron Scattering and Computer Simulations

Carla Andreani, Giovanni Romanelli, Alexandra Parmentier, Roberto Senesi, Alexander I Kolesnikov, Hsin-Yu Ko, Marcos F Calegari Andrade, Roberto Car
J. Phys. Chem. Lett., 2020, 11, 9461–9467.
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A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases

R. Li, E. Lee, T. Luo
Materials Today Physics, 2020, 12, 100181.
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Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy

Zhilong Wang, Yanqiang Han, Jinjin Li, Xiao He
J. Phys. Chem. B, 2020, 124, 3027–3035.
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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.
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Development of interatomic potential for Al-Tb alloys using a deep neural network learning method

L Tang, Z J Yang, T Q Wen, K M Ho, M J Kramer, C Z Wang
Phys. Chem. Chem. Phys., 2020, 22, 18467–18479.
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Isotope effects in x-ray absorption spectra of liquid water

Chunyi Zhang, Linfeng Zhang, Jianhang Xu, Fujie Tang, Biswajit Santra, Xifan Wu
Phys. Rev. B, 2020, 102, 115155.
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Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics

Yuzhi Zhang, Chang Gao, Qianrui Liu, Linfeng Zhang, Han Wang, Mohan Chen
Physics of Plasmas, 2020, 27, 122704.
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Molecular Dynamics Simulations of Molten Magnesium Chloride Using Machine-Learning-Based Deep Potential

Wenshuo Liang, Guimin Lu, Jianguo Yu
Adv. Theory Simul., 2020, 3, 2000180.
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Grain boundary strengthening in ZrB2 by segregation of W: Atomistic simulations with deep learning potential

Fu-Zhi Dai, Bo Wen, Huimin Xiang, Yanchun Zhou
Journal of the European Ceramic Society, 2020, 40, 5029–5036.
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Crystal Structure Prediction of Binary Alloys via Deep Potential

Haidi Wang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Front. Chem., 2020, 8, 589795.
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Deep machine learning interatomic potential for liquid silica

I A Balyakin, S V Rempel, R E Ryltsev, A A Rempel
Phys. Rev. E, 2020, 102, 52125.
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Structure of disorderedTiO2phases from ab initio based deep neural network simulations

Marcos F. Calegari Andrade, Annabella Selloni
Phys. Rev. Materials, 2020, 4, 113803.
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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.
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2019

Active learning of uniformly accurate interatomic potentials for materials simulation

Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, Weinan E
Phys. Rev. Materials, 2019, 3, 23804.
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Isotope effects in liquid water via deep potential molecular dynamics

Hsin-Yu Ko, Linfeng Zhang, Biswajit Santra, Han Wang, Weinan E, Robert A. DiStasio Jr, Roberto Car
Molecular Physics, 2019, 117, 3269–3281.
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Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds

Tongqi Wen, Cai-Zhuang Wang, M. J. Kramer, Yang Sun, Beilin Ye, Haidi Wang, Xueyuan Liu, Chao Zhang, Feng Zhang, Kai-Ming Ho, Nan Wang
Phys. Rev. B, 2019, 100, 174101.
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Deep learning inter-atomic potential model for accurate irradiation damage simulations

Hao Wang, Xun Guo, Linfeng Zhang, Han Wang, Jianming Xue
Appl. Phys. Lett., 2019, 114, 244101.
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2018

Silicon Liquid Structure and Crystal Nucleation from Ab~Initio Deep Metadynamics

Luigi Bonati, Michele Parrinello
Phys. Rev. Lett., 2018, 121, 265701.
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Deep Learning for Nonadiabatic Excited-State Dynamics

Wen-Kai Chen, Xiang-Yang Liu, Wei-Hai Fang, Pavlo O Dral, Ganglong Cui
J. Phys. Chem. Lett., 2018, 9, 6702–6708.
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Adaptive coupling of a deep neural network potential to a classical force field

Linfeng Zhang, Han Wang, Weinan E
J. Chem. Phys., 2018, 149, 154107.
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DeePCG: Constructing coarse-grained models via deep neural networks

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E
J. Chem. Phys., 2018, 149, 34101.
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DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Han Wang, Linfeng Zhang, Jiequn Han, Weinan E
Computer Physics Communications, 2018, 228, 178–184.
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Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E
Phys. Rev. Lett., 2018, 120, 143001.
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