The following publications have used the DeePMD-kit software. Publications that only mentioned the DeePMD-kit will not be included below.
2023
Grain boundaries induce significant decrease in lattice thermal conductivity of CdTe
Xiaona Huang, Kun Luo, Yidi Shen, Yanan Yue, Qi An
Energy and AI, 2023, 11, 100210.
DOI: 10.1016/j.egyai.2022.100210
Revisiting the structure, interaction, and dynamical property of ionic liquid from the deep learning force field
Yulong Ling, Kun Li, Mi Wang, Junfeng Lu, Chenlu Wang, Yanlei Wang, Hongyan He
Journal of Power Sources, 2023, 555, 232350.
DOI: 10.1016/j.jpowsour.2022.232350
Quasiplastic deformation in shocked nanocrystalline boron carbide: Grain boundary sliding and local amorphization
Jun Li, Qi An
Journal of the European Ceramic Society, 2023, 43, 208–216.
DOI: 10.1016/j.jeurceramsoc.2022.10.014
Accurate Fe-He machine learning potential for studying He effects in BCC-Fe
Krishna Chaitanya Pitike, Wahyu Setyawan
Journal of Nuclear Materials, 2023, 574, 154183.
DOI: 10.1016/j.jnucmat.2022.154183
Solvation structures of calcium and magnesium ions in water with the presence of hydroxide: a study by deep potential molecular dynamics
Jianchuan Liu, Renxi Liu, Yu Cao, Mohan Chen
Phys. Chem. Chem. Phys., 2023.
DOI: 10.1039/d2cp04105g
Grain boundary sliding and distortion on a nanosecond timescale induce trap states in CsPbBr3: ab initio investigation with machine learning force field
Dongyu Liu, Yifan Wu, Andrey S Vasenko, Oleg V Prezhdo
Nanoscale, 2023, 15, 285–293.
DOI: 10.1039/d2nr05918e
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
Modeling Chemical Reactions in Alkali Carbonate-Hydroxide Electrolytes with Deep Learning Potentials
Anirban Mondal, Dina Kussainova, Shuwen Yue, Athanassios Z Panagiotopoulos
J. Chem. Theory Comput., 2022.
DOI: 10.1021/acs.jctc.2c00816
Spatial arrangement of dynamic surface species from solid-state NMR and machine learning-accelerated MD simulations
Takeshi Kobayashi, Da-Jiang Liu, Fr'ed'eric A Perras
Chem. Commun. (Camb)., 2022, 58, 13939–13942.
DOI: 10.1039/d2cc05861h
Fluctuations at Metal Halide Perovskite Grain Boundaries Create Transient Trap States: Machine Learning Assisted Ab Initio Analysis
Yifan Wu, Dongyu Liu, Weibin Chu, Bipeng Wang, Andrey S Vasenko, Oleg V Prezhdo
ACS Appl. Mater. Interfaces, 2022, 14, 55753–55761.
DOI: 10.1021/acsami.2c16203
Modeling Short-Range and Three-Membered Ring Structures in Lithium Borosilicate Glasses Using a Machine-Learning Potential
Shingo Urata
J. Phys. Chem. C, 2022, 126, 21507–21517.
DOI: 10.1021/acs.jpcc.2c07597
Lattice Thermal Conductivity of MgSiO3 Perovskite and Post- Perovskite under Lower Mantle Conditions Calculated by Deep Potential Molecular Dynamics
Fenghu Yang, Qiyu Zeng, Bo Chen, Dongdong Kang, Shen Zhang, Jianhua Wu, Xiaoxiang Yu, Jiayu Dai
Chinese Phys. Lett., 2022, 39, 116301.
DOI: 10.1088/0256-307X/39/11/116301
Origin of the herringbone reconstruction of Au(111) surface at the atomic scale
Pai Li, Feng Ding
Sci. Adv., 2022, 8, eabq2900.
DOI: 10.1126/sciadv.abq2900
Resolving the odd-even oscillation of water dissociation at rutile TiO2(110)-water interface by machine learning accelerated molecular dynamics
Yong-Bin Zhuang, Rui-Hao Bi, Jun Cheng
J. Chem. Phys., 2022, 157, 164701.
DOI: 10.1063/5.0126333
Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles
Marco Fronzi, Roger D Amos, Rika Kobayashi, Naoki Matsumura, Kenta Watanabe, Rafael K Morizawa
Nanomaterials (Basel)., 2022, 12, 3891.
DOI: 10.3390/nano12213891
Predicted superconductivity and superionic state in the electride Li5N under high pressure
Zhongyu Wan, Chao Zhang, Tianyi Yang, Wenjun Xu, Ruiqin Zhang
New J. Phys., 2022, 24, 113012.
DOI: 10.1088/1367-2630/ac9cff
Origin of negative thermal expansion and pressure-induced amorphization in zirconium tungstate from a machine-learning potential
Ri He, Hongyu Wu, Yi Lu, Zhicheng Zhong
Phys. Rev. B, 2022, 106, 174101.
DOI: 10.1103/PhysRevB.106.174101
Phonon Thermal Transport in
Robert D McMichael, Sean M Blakley
Phys. Rev. Appl., 2022, 18, 54022.
DOI: 10.1103/PhysRevApplied.18.054022
Piezo- and Pyroelectricity in Zirconia: A Study with Machine-Learned Force Fields
Robert D McMichael, Sean M Blakley
Phys. Rev. Appl., 2022, 18, 54066.
DOI: 10.1103/PhysRevApplied.18.054066
Classical and machine learning interatomic potentials for BCC vanadium
Rui Wang, Xiaoxiao Ma, Linfeng Zhang, Han Wang, David J. Srolovitz, Tongqi Wen, Zhaoxuan Wu
Phys. Rev. Materials, 2022, 6, 113603.
DOI: 10.1103/PhysRevMaterials.6.113603
Order of magnitude reduction in Joule heating of single molecular junctions between graphene electrodes
Gen Li, Bing-Zhong Hu, Wen-Hao Mao, Nuo Yang, Jing-Tao L"u
J. Chem. Phys., 2022, 157, 174303.
DOI: 10.1063/5.0118952
Plastic deformation of superionic water ices
Filipe Matusalem, J'essica Santos Rego, Maurice de Koning
Proc. Natl. Acad. Sci. U. S. A., 2022, 119, e2203397119.
DOI: 10.1073/pnas.2203397119
Metal Affinity of Support Dictates Sintering of Gold Catalysts
Jin-Cheng Liu, Langli Luo, Hai Xiao, Junfa Zhu, Yang He, Jun Li
J. Am. Chem. Soc., 2022, 144, 20601–20609.
DOI: 10.1021/jacs.2c06785
Multireference Generalization of the Weighted Thermodynamic Perturbation Method
Timothy J Giese, Jinzhe Zeng, Darrin M York
J. Phys. Chem. A, 2022, 126, 8519–8533.
DOI: 10.1021/acs.jpca.2c06201
Thermal Conductivity of Hydrous Wadsleyite Determined by Non-Equilibrium Molecular Dynamics Based on Machine Learning
Dong Wang, Zhongqing Wu, Xin Deng
Geophysical Research Letters, 2022, 49.
DOI: 10.1029/2022GL100337
DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials
Wenfei Li, Qi Ou, Yixiao Chen, Yu Cao, Renxi Liu, Chunyi Zhang, Daye Zheng, Chun Cai, Xifan Wu, Han Wang, Mohan Chen, Linfeng Zhang
J. Phys. Chem. A, 2022, 126, 9154–9164.
DOI: 10.1021/acs.jpca.2c05000
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-Water Interfaces Depend on Depth and on Interface Specific Area
Miguel de la Puente, Rolf David, Axel Gomez, Damien Laage
J. Am. Chem. Soc., 2022, 144, 10524–10529.
DOI: 10.1021/jacs.2c03099
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
Machine learning builds full-QM precision protein force fields in seconds
Yanqiang Han, Zhilong Wang, Zhiyun Wei, Jinyun Liu, Jinjin Li
Brief. Bioinform., 2021, 22.
DOI: 10.1093/bib/bbab158
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
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J. Chem. Theory Comput., 2021, 17, 5745–5758.
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