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.

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Last update date: Nov 28, 2023

2024

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

Mobile dislocation mediated Hall-Petch and inverse Hall-Petch behaviors in nanocrystalline Al-doped boron carbide

Jun Li, Kun Luo, Qi An
Journal of the European Ceramic Society, 2024, 44, 659–667.
DOI: 10.1016/j.jeurceramsoc.2023.09.079

2023

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-based potential developed for calcium silicate hydrates with both high accuracy and efficiency

Weihuan Li, Yang Zhou, Li Ding, Pengfei Lv, Yifan Su, Rui Wang, Changwen Miao
Journal of Sustainable Cement-Based Materials, 2023, 12, 1335–1346.
DOI: 10.1080/21650373.2023.2219251

Tuning the lattice thermal conductivity of Sb₂Te₃ by Cr doping: a deep potential molecular dynamics study

Pan Zhang, Wenkai Liao, Ziyang Zhu, Mi Qin, Zhenhua Zhang, Dan Jin, Yong Liu, Ziyu Wang, Zhihong Lu, Rui Xiong
Phys. Chem. Chem. Phys., 2023, 25, 15422–15432.
DOI: 10.1039/d3cp00999h

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

A deep potential molecular dynamics study on the ionic structure and transport properties of NaCl-CaCl2 molten salt

Gegentana, Liu Cui, Leping Zhou, Xiaoze Du
Ionics, 2023, 1–11.

A first-principles machine-learning force field for heterogeneous ice nucleation on microcline feldspar

Pablo M Piaggi, Annabella Selloni, Athanassios Z Panagiotopoulos, Roberto Car, Pablo G Debenedetti
Faraday Discuss., 2023.
DOI: 10.1039/d3fd00100h

Accelerating materials discovery using integrated deep machine learning approaches

Weiyi Xia, Ling Tang, Huaijun Sun, Chao Zhang, Kai-Ming Ho, Gayatri Viswanathan, Kirill Kovnir, Cai-Zhuang Wang
J. Mater. Chem. A, 2023.
DOI: 10.1039/d3ta03771a

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

Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials

Nikhil V S Avula, Michael L Klein, Sundaram Balasubramanian
J. Phys. Chem. Lett., 2023, 14, 9500–9507.
DOI: 10.1021/acs.jpclett.3c02112

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

Water dissociation at the water-rutile TiO ₂ (110) interface from ab~initio-based deep neural network simulations

Bo Wen, Marcos F Calegari Andrade, Li-Min Liu, Annabella Selloni
Proc. Natl. Acad. Sci. U. S. A., 2023, 120, e2212250120.
DOI: 10.1073/pnas.2212250120

Development and Validation of Versatile Deep Atomistic Potentials for Metal Oxides

Pandu Wisesa, Christopher M Andolina, Wissam A Saidi
J. Phys. Chem. Lett., 2023, 14, 468–475.
DOI: 10.1021/acs.jpclett.2c03445

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

Accessing the thermal conductivities of Sb₂Te₃ and Bi₂Te₃/Sb₂Te₃ superlattices by molecular dynamics simulations with a deep neural network potential

Pan Zhang, Mi Qin, Zhenhua Zhang, Dan Jin, Yong Liu, Ziyu Wang, Zhihong Lu, Jing Shi, Rui Xiong
Phys. Chem. Chem. Phys., 2023, 25, 6164–6174.
DOI: 10.1039/d2cp05590b

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials

Tyler G Sours, Ambarish R Kulkarni
J. Phys. Chem. C. Nanomater. Interfaces, 2023, 127, 1455–1463.
DOI: 10.1021/acs.jpcc.2c08429

Melting of MgSiO3 determined by machine learning potentials

Jie Deng, Haiyang Niu, Junwei Hu, Mingyi Chen, Lars Stixrude
Phys. Rev. B, 2023, 107, 64103.
DOI: 10.1103/PhysRevB.107.064103

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

Songyuan Yao, Richard Van, Xiaoliang Pan, Ji Hwan Park, Yuezhi Mao, Jingzhi Pu, Ye Mei, Yihan Shao
RSC Adv., 2023, 13, 4565–4577.
DOI: 10.1039/d2ra08180f

Large-scale atomistic simulation of dislocation core structure in face-centered cubic metal with Deep Potential method

Fenglin Deng, Hongyu Wu, Ri He, Peijun Yang, Zhicheng Zhong
Computational Materials Science, 2023, 218, 111941.
DOI: 10.1016/j.commatsci.2022.111941

Melting conditions and entropies of superionic water ice: Free-energy calculations based on hybrid solid/liquid reference systems

Vitor Fidalgo C\^andido, Filipe Matusalem, Maurice de Koning
J. Chem. Phys., 2023, 158, 64502.
DOI: 10.1063/5.0138987

Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water

M'arcio S Gomes-Filho, Alberto Torres, Alexandre Reily Rocha, Luana S Pedroza
J. Phys. Chem. B, 2023, 127, 1422–1428.
DOI: 10.1021/acs.jpcb.2c09059

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

Quasiclassical Trajectory Simulation as a Protocol to Build Locally Accurate Machine Learning Potentials

Jintu Zhang, Haotian Zhang, Zhixin Qin, Yu Kang, Xin Hong, Tingjun Hou
J. Chem. Inf. Model., 2023, 63, 1133–1142.
DOI: 10.1021/acs.jcim.2c01497

QD$\pi$: A Quantum Deep Potential Interaction Model for Drug Discovery

Jinzhe Zeng, Yujun Tao, Timothy J Giese, Darrin M York
J. Chem. Theory Comput., 2023, 19, 1261–1275.
DOI: 10.1021/acs.jctc.2c01172

A deep learning approach to predict thermophysical properties of metastable liquid Ti-Ni-Cr-Al alloy

R. L. Xiao, Q. Wang, J. Y. Qin, J. F. Zhao, Y. Ruan, H. P. Wang, H. Li, B. Wei
2023, 133.
DOI: 10.1063/5.0138001

A \textquotedblleftshort blanket\textquotedblright dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?

Yaoguang Zhai, Alessandro Caruso, Sigbj\orn L\oland Bore, Zhishang Luo, Francesco Paesani
J. Chem. Phys., 2023, 158, 84111.
DOI: 10.1063/5.0142843

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

Oxygen Vacancy Diffusion in Rutile TiO₂: Insight from Deep Neural Network Potential Simulations

Zhihong Wu, Wen-Jin Yin, Bo Wen, Dongwei Ma, Li-Min Liu
J. Phys. Chem. Lett., 2023, 14, 2208–2214.
DOI: 10.1021/acs.jpclett.2c03827

Nanotwinning-induced pseudoplastic deformation in boron carbide under low temperature

Jun Li, Qi An
International Journal of Mechanical Sciences, 2023, 242, 107998.
DOI: 10.1016/j.ijmecsci.2022.107998

Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO₂\textquoterights Chemisorption and Diffusion in Mg-MOF-74

Bowen Zheng, Felipe Lopes Oliveira, Rodrigo Neumann Barros Ferreira, Mathias Steiner, Hendrik Hamann, Grace X Gu, Binquan Luan
ACS Nano, 2023, 17, 5579–5587.
DOI: 10.1021/acsnano.2c11102

Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states

Jinzhe Zeng, Yujun Tao, Timothy J Giese, Darrin M York
J. Chem. Phys., 2023, 158, 124110.
DOI: 10.1063/5.0139281

Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments

Matteo Cioni, Daniela Polino, Daniele Rapetti, Luca Pesce, Massimo Delle Piane, Giovanni M Pavan
J. Chem. Phys., 2023, 158, 124701.
DOI: 10.1063/5.0139010

Prediction on local structure and properties of LiCl-KCl-AlCl3 ternary molten salt with deep learning potential

Min Bu, Taixi Feng, Guimin Lu
Journal of Molecular Liquids, 2023, 375, 120689.
DOI: 10.1016/j.molliq.2022.120689

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

High-Accuracy Neural Network Interatomic Potential for Silicon Nitride

Hui Xu, Zeyuan Li, Zhaofu Zhang, Sheng Liu, Shengnan Shen, Yuzheng Guo
Nanomaterials (Basel)., 2023, 13, 1352.
DOI: 10.3390/nano13081352

A New Spinel Chloride Solid Electrolyte with High Ionic Conductivity and Stability for Na-Ion Batteries

Jiahui Liu, Shuo Wang, Yoshiyuki Kawazoe, Qiang Sun
ACS Materials Lett., 2023, 5, 1009–1017.
DOI: 10.1021/acsmaterialslett.3c00119

Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects

Th'eo Jaffrelot Inizan, Thomas Pl'e, Olivier Adjoua, Pengyu Ren, Hatice G"okcan, Olexandr Isayev, Louis Lagard\`ere, Jean-Philip Piquemal
Chem. Sci., 2023, 14, 5438–5452.
DOI: 10.1039/d2sc04815a

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

Microstructure and Thermophysical Property Prediction for Chloride Composite Phase Change Materials: A Deep Potential Molecular Dynamics Study

C. Y. Zhao, Y. B. Tao, Y. He
J. Phys. Chem. C, 2023, 127, 6852–6860.
DOI: 10.1021/acs.jpcc.2c08589

Cooperative diffusion in body-centered cubic iron in Earth and super- Earths\textquoteright inner core conditions

Maitrayee Ghosh, Shuai Zhang, Lianming Hu, S X Hu
J. Phys. Condens. Matter, 2023, 35, 154002.
DOI: 10.1088/1361-648X/acba71

Pressure-Induced Stability of Methane Hydrate from Machine Learning Force Field Simulations

Kun Luo, Yidi Shen, Jun Li, Qi An
J. Phys. Chem. C, 2023, 127, 7071–7077.
DOI: 10.1021/acs.jpcc.2c09121

Unraveling the Dynamic Correlations between Transition Metal Migration and the Oxygen Dimer Formation in the Highly Delithiated Li_xCoO₂ 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

Predicting thermodynamic stability of magnesium alloys in machine learning

Xi He, Jinde Liu, Chen Yang, Gang Jiang
Computational Materials Science, 2023, 223, 112111.
DOI: 10.1016/j.commatsci.2023.112111

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

Estimation of frequency factors for the calculation of kinetic isotope effects from classical and path integral free energy simulations

Timothy J Giese, Darrin M York
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0147218

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

Characterizing Structure-Dependent TiS₂/Water Interfaces Using Deep-Neural-Network-Assisted Molecular Dynamics

Lesheng Li, Marcos F. Calegari Andrade, Roberto Car, Annabella Selloni, Emily A. Carter
J. Phys. Chem. C, 2023, 127, 9750–9758.
DOI: 10.1021/acs.jpcc.2c08581

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

Machine learning insight into h-BN growth on Pt(111) from atomic states

Kangmo Yeo, Sukmin Jeong
Applied Surface Science, 2023, 621, 156893.
DOI: 10.1016/j.apsusc.2023.156893

Molecular dynamics simulations of CaCl2-NaCl molten salt based on the machine learning potentials

Yun Xie, Min Bu, Guiming Zou, Ye Zhang, Guimin Lu
Solar Energy Materials and Solar Cells, 2023, 254, 112275.
DOI: 10.1016/j.solmat.2023.112275

A Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics

Zhuoying Zhao, Ziling Tan, Pinghui Mo, Xiaonan Wang, Dan Zhao, Xin Zhang, Ming Tao, Jie Liu
IEEE Trans. Circuits Syst. I, 2023, 70, 2439–2449.
DOI: 10.1109/TCSI.2023.3255199

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

Reversible densification and cooperative atomic movement induced \textquotedblleftcompaction\textquotedblright in vitreous silica: a new sight from deep neural network interatomic potentials

Yongnian Qi, Xiaoguang Guo, Hao Wang, Shuohua Zhang, Ming Li, Ping Zhou, Dongming Guo
J Mater Sci, 2023, 58, 9515–9532.
DOI: 10.1007/s10853-023-08599-w

Molecular dynamics simulation of Fe-Si alloys using a neural network machine learning potential

Huaijun Sun, Chao Zhang, Ling Tang, Renhai Wang, Weiyi Xia, Cai-Zhuang Wang
Phys. Rev. B, 2023, 107, 224301.
DOI: 10.1103/PhysRevB.107.224301

Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles\textemdashTransferability towards Bulk

Marco Fronzi, Roger D Amos, Rika Kobayashi
Nanomaterials (Basel)., 2023, 13, 1832.
DOI: 10.3390/nano13121832

A genome dependence of metastable phase selection on atomic structure for undercooled liquid Nb90Si10 hypoeutectic alloy

Q. Wang, C. H. Zheng, M. X. Li, L. Hu, H. P. Wang, B. Wei
2023, 122.
DOI: 10.1063/5.0152293

TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra

Cristina Caruso, Annalisa Cardellini, Martina Crippa, Daniele Rapetti, Giovanni M Pavan
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0147025

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

Temperature-dependent microwave dielectric permittivity of gallium oxide: A deep potential molecular dynamics study

Zhiqiang Li, Xinlei Duan, Linhua Liu, Jia-Yue Yang
2023, 133.
DOI: 10.1063/5.0149447

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

Probing Confinement Effects on the Infrared Spectra of Water with Deep Potential Molecular Dynamics Simulations

Marcos F Calegari Andrade, Tuan Anh Pham
J. Phys. Chem. Lett., 2023, 14, 5560–5566.
DOI: 10.1021/acs.jpclett.3c01054

A Deep Neural Network Potential to Study the Thermal Conductivity of MnBi2Te4 and Bi2Te3/MnBi2Te4 Superlattice

Ruijin Qu, Yawei Lv, Zhihong Lu
J. Electron. Mater., 2023, 52, 4475–4483.
DOI: 10.1007/s11664-023-10403-z

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

Modelling of dislocations, twins and crack-tips in HCP and BCC Ti

Tongqi Wen, Anwen Liu, Rui Wang, Linfeng Zhang, Jian Han, Han Wang, David J. Srolovitz, Zhaoxuan Wu
International Journal of Plasticity, 2023, 166, 103644.
DOI: 10.1016/j.ijplas.2023.103644

Halide Vacancies Create No Charge Traps on Lead Halide Perovskite Surfaces but Can Generate Deep Traps in the Bulk

Jingyi Ran, Bipeng Wang, Yifan Wu, Dongyu Liu, Carlos Mora Perez, Andrey S Vasenko, Oleg V Prezhdo
J. Phys. Chem. Lett., 2023, 14, 6028–6036.
DOI: 10.1021/acs.jpclett.3c01231

High-Throughput Condensed-Phase Hybrid Density Functional Theory for Large-Scale Finite-Gap Systems: The SeA Approach

Hsin-Yu Ko, Marcos F Calegari Andrade, Zachary M Sparrow, Ju-An Zhang, Robert A DiStasio Jr
J. Chem. Theory Comput., 2023, 19, 4182–4201.
DOI: 10.1021/acs.jctc.2c00827

Microscopic Mechanism of Proton Transfer in Pure Water under Ambient Conditions

Jun Huo, Jianghao Chen, Pei Liu, Benkun Hong, Jian Zhang, Hao Dong, Shuhua Li
J. Chem. Theory Comput., 2023, 19, 4243–4254.
DOI: 10.1021/acs.jctc.3c00244

Revealing Carbon Vacancy Distribution on $\alpha$-MoC_1-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

Modeling the aqueous interface of amorphous TiO2 using deep potential molecular dynamics

Zhutian Ding, Annabella Selloni
J. Chem. Phys., 2023, 159.
DOI: 10.1063/5.0157188

Transferability evaluation of the deep potential model for simulating water-graphene confined system

Dongfei Liu, Jianzhong Wu, Diannan Lu
J. Chem. Phys., 2023, 159.
DOI: 10.1063/5.0153196

Structural transformations in single-crystalline AgPd nanoalloys from multiscale deep potential molecular dynamics

Longfei Guo, Tao Jin, Shuang Shan, Quan Tang, Zhen Li, Chongyang Wang, Junpeng Wang, Bowei Pan, Qiao Wang, Fuyi Chen
J. Chem. Phys., 2023, 159.
DOI: 10.1063/5.0158918

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

Martina Crippa, Annalisa Cardellini, Cristina Caruso, Giovanni M Pavan
Proc. Natl. Acad. Sci. U. S. A., 2023, 120, e2300565120.
DOI: 10.1073/pnas.2300565120

Anisotropic Collective Variables with Machine Learning Potential for Ab Initio Crystallization of Complex Ceramics

Yuanpeng Deng, Shubin Fu, Jingran Guo, Xiang Xu, Hui Li
ACS Nano, 2023, 17, 14099–14113.
DOI: 10.1021/acsnano.3c04602

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

Rapid acquisition of liquid thermophysical properties from pure metals to quaternary alloys by proposing a machine learning strategy

R. L. Xiao, K. L. Liu, Y. Ruan, B. Wei
2023, 123.
DOI: 10.1063/5.0160046

Extreme phonon anharmonicity underpins superionic diffusion and ultralow thermal conductivity in argyrodite Ag8SnSe6

Qingyong Ren, Mayanak K Gupta, Min Jin, Jingxuan Ding, Jiangtao Wu, Zhiwei Chen, Siqi Lin, Oscar Fabelo, Jose Alberto Rodr'\iguez-Velamaz'an, Maiko Kofu, Kenji Nakajima, Marcell Wolf, Fengfeng Zhu, Jianli Wang, Zhenxiang Cheng, Guohua Wang, Xin Tong, Yanzhong Pei, Olivier Delaire, Jie Ma
Nat. Mater., 2023, 22, 999–1006.
DOI: 10.1038/s41563-023-01560-x

Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table

Christopher M. Andolina, Wissam A. Saidi
Digital Discovery, 2023, 2, 1070–1077.
DOI: 10.1039/d3dd00046j

Unraveling the Oxidation Behaviors of MXenes in Aqueous Systems by Active-Learning-Potential Molecular-Dynamics Simulation

Pengfei Hou, Yumiao Tian, Yu Xie, Fei Du, Gang Chen, Aleksandra Vojvodic, Jianzhong Wu, Xing Meng
Angew. Chem. Int. Ed. Engl., 2023, 62, e202304205.
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DeePMD-kit v2: A software package for deep potential models

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

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A Deep Neural Network Interface Potential for Li-Cu Systems

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

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

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A Deep Neural Network Potential for Water Confined in Graphene Nanocapillaries

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Dissolving salt is not equivalent to applying a pressure on water

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

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

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

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Self-Healing Mechanism of Lithium in Lithium Metal

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A deep learning interatomic potential developed for atomistic simulation of carbon materials

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

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

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

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

Xiaona Huang, Yidi Shen, Qi An
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A deep potential model with long-range electrostatic interactions

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

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

R.E. Ryltsev, N.M. Chtchelkatchev
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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
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Molecular dynamics simulation of molten strontium chloride based on deep potential

Di Guo, Jia Zhao, Wenshuo Liang, Guimin Lu
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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
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A deep learning potential applied in tobermorite phases and extended to calcium silicate hydrates

Yang Zhou, Haojie Zheng, Weihuan Li, Tao Ma, Changwen Miao
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Deep learning potential for superionic phase of Ag2S

I.A. Balyakin, S.I. Sadovnikov
Computational Materials Science, 2022, 202, 110963.
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Neural network representation of electronic structure from ab initio molecular dynamics

Qiangqiang Gu, Linfeng Zhang, Ji Feng
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Machine learning builds full-QM precision protein force fields in seconds

Yanqiang Han, Zhilong Wang, Zhiyun Wei, Jinyun Liu, Jinjin Li
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Efficiently Trained Deep Learning Potential for Graphane

Siddarth K. Achar, Linfeng Zhang, J. Karl Johnson
J. Phys. Chem. C, 2021, 125, 14874–14882.
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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
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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
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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
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Accurate force field of two-dimensional ferroelectrics from deep learning

Jing Wu, Liyi Bai, Jiawei Huang, Liyang Ma, Jian Liu, Shi Liu
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Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator

Jinzhe Zeng, Linfeng Zhang, Han Wang, Tong Zhu
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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
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Reactive uptake of N₂O₅ by atmospheric aerosol is dominated by interfacial processes

Mirza Galib, David T Limmer
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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
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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
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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.
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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
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Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

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

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

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Choosing the right molecular machine learning potential

Max Pinheiro Jr, Fuchun Ge, Nicolas Ferr'e, Pavlo O Dral, Mario Barbatti
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Atomic structure of liquid refractory Nb₅Si₃ intermetallic compound alloy based upon deep neural network potential

Q. Wang, B. Zhai, H. P. Wang, B. Wei
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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
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Resolving the Structural Debate for the Hydrated Excess Proton in Water

Paul B Calio, Chenghan Li, Gregory A Voth
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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
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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.
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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
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Learning intermolecular forces at liquid-vapor interfaces

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

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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
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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
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Thermal Conductivity of Silicate Liquid Determined by Machine Learning Potentials

Jie Deng, Lars Stixrude
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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
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Liquid-Liquid Critical Point in Phosphorus

Manyi Yang, Tarak Karmakar, Michele Parrinello
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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
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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
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Anomalous Behavior of Viscosity and Electrical Conductivity of MgSiO ₃ Melt at Mantle Conditions

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Deep neural network potentials for diffusional lithium isotope fractionation in silicate melts

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Phase Diagram of a Deep Potential Water Model

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Theoretical prediction on the local structure and transport properties of molten alkali chlorides by deep potentials

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

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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
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Theoretical prediction on the redox potentials of rare-earth ions by deep potentials

Jia Zhao, Wenshuo Liang, Guimin Lu
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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 ferroelectric HfO2

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 (Zr_0.2Hf_0.2Ti_0.2Nb_0.2Ta_0.2)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.
DOI: 10.1016/j.jmst.2020.01.005

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 disordered TiO2 phases 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.
DOI: 10.1073/pnas.2015440117

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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