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: June 30, 2024

2024

Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter

Tao Chen, Qianrui Liu, Yu Liu, Liang Sun, Mohan Chen
Matter Radiat. Extrem., 2024, 9 (1).
DOI: 10.1063/5.0163303

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

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

Polarization-driven band topology evolution in twisted MoTe2 and WSe2

Xiao-Wei Zhang, Chong Wang, Xiaoyu Liu, Yueyao Fan, Ting Cao, Di Xiao
Nat. Commun., 2024, 15 (1), 4223.
DOI: 10.1038/s41467-024-48511-x

Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations

Daniel Hedman, Ben McLean, Christophe Bichara, Shigeo Maruyama, J. Andreas Larsson, Feng Ding
Nat. Commun., 2024, 15 (1), 4076.
DOI: 10.1038/s41467-024-47999-7

Complexity of many-body interactions in transition metals via machine- learned force fields from the TM23 data set

Cameron J. Owen, Steven B. Torrisi, Yu Xie, Simon Batzner, Kyle Bystrom, Jennifer Coulter, Albert Musaelian, Lixin Sun, Boris Kozinsky
Npj Comput. Mater, 2024, 10 (1), 92.
DOI: 10.1038/s41524-024-01264-z

Unconventional mechanical and thermal behaviours of MOF CALF-20

Dong Fan, Supriyo Naskar, Guillaume Maurin
Nat. Commun., 2024, 15 (1), 3251.
DOI: 10.1038/s41467-024-47695-6

Realization of sextuple polarization states and interstate switching in antiferroelectric CuInP2S6

Tao Li, Yongyi Wu, Guoliang Yu, Shengxian Li, Yifeng Ren, Yadong Liu, Jiarui Liu, Hao Feng, Yu Deng, Mingxing Chen, Zhenyu Zhang, Tai Min
Nat. Commun., 2024, 15 (1), 2653.
DOI: 10.1038/s41467-024-46891-8

Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change compound with a machine-learned interatomic potential

Omar {Abou El Kheir}, Luigi Bonati, Michele Parrinello, Marco Bernasconi
Npj Comput. Mater, 2024, 10 (1), 33.
DOI: 10.1038/s41524-024-01217-6

Principal component analysis enables the design of deep learning potential precisely capturing LLZO phase transitions

Yiwei You, Dexin Zhang, Fulun Wu, Xinrui Cao, Yang Sun, Zi-Zhong Zhu, Shunqing Wu
Npj Comput. Mater, 2024, 10 (1), 57.
DOI: 10.1038/s41524-024-01240-7

Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling

Ji Qi, Tsz Wai Ko, Brandon C. Wood, Tuan Anh Pham, Shyue Ping Ong
Npj Comput. Mater, 2024, 10 (1), 43.
DOI: 10.1038/s41524-024-01227-4

Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks

Sandro Wieser, Egbert Zojer
Npj Comput. Mater, 2024, 10 (1), 18.
DOI: 10.1038/s41524-024-01205-w

Enhancing ReaxFF for molecular dynamics simulations of lithium-ion batteries: an interactive reparameterization protocol

Paolo {De Angelis}, Roberta Cappabianca, Matteo Fasano, Pietro Asinari, Eliodoro Chiavazzo
Sci. Rep., 2024, 14 (1), 978.
DOI: 10.1038/s41598-023-50978-5

Ce-doped copper oxide and copper vanadate Cu3VO4 hybrid for boosting nitrate electroreduction to ammonia

Meng Zhang, Yang Liu, Yun Duan, Xu Liu, Yan-Qin Wang
J. Colloid Interface Sci., 2024, 671, 258–269.
DOI: 10.1016/j.jcis.2024.05.189

Construction and application of deep learning potential for CaO under high pressure

Xinwei Wang, Zi-Jiang Liu, Jin-Shan Feng, Meng-Ru Chen, Liang Li, Xiao-Wei Sun, Fubo Tian
Comput. Mater. Sci., 2024, 244, 113154.
DOI: 10.1016/j.commatsci.2024.113154

Pressure-driven enhancement of phonon contribution to the thermal conductivity of Iridium

Niraj Bhatt, Pravin Karna, Sandip Thakur, Ashutosh Giri
Int. J. Heat Mass Transf., 2024, 229, 125673.
DOI: 10.1016/j.ijheatmasstransfer.2024.125673

Selective activation of methane on hydroxyapatite surfaces: Insights from machine learning and density functional theory

Jing Wang, Xinrong Yan, Xin Wang, Mingli Yang, Dingguo Xu
Nano Energy, 2024, 127, 109762.
DOI: 10.1016/j.nanoen.2024.109762

Machine learning model to efficiently predict the structure and properties of MgCl2-NaCl-KCl melts

Taixi Feng, Jia Zhao, Guimin Lu
Sol. Energy Mater. Sol. Cells, 2024, 272, 112903.
DOI: 10.1016/j.solmat.2024.112903

Exploring dielectric properties in atomistic models of amorphous boron nitride

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

Dual-channel phonon transport leads to low thermal conductivity in pyrochlore La2Hf2O7

Junwei Che, Wenjie Huang, Guoliang Ren, Jiajun Linghu, Xuezhi Wang
Ceram. Int., 2024, 50 (13), 22865–22873.
DOI: 10.1016/j.ceramint.2024.04.011

Unveiling the crystallization mechanism of cadmium selenide via molecular dynamics simulation with machine-learning-based deep potential

Linshuang Zhang, Manyi Yang, Shiwei Zhang, Haiyang Niu
J. Mater. Sci. Technol., 2024, 185, 23–31.
DOI: 10.1016/j.jmst.2023.09.059

The nucleation and growth mechanism of solid-state amorphization and diffusion behavior at the W-Cu interface

Kai Wang, Guoqing Yao, Mengwei Lv, Zumin Wang, Yuan Huang, Wei Xi
Compos. B: Eng., 2024, 279, 111452.
DOI: 10.1016/j.compositesb.2024.111452

Machine learning molecular dynamics insight into high interface stability and fast kinetics of low-cost magnesium chloride amine electrolyte for rechargeable magnesium batteries

Haiming Hua, Fei Wang, Feng Wang, Jiayue Wu, Yaoqi Xu, Yichao Zhuang, Jing Zeng, Jinbao Zhao
Energy Storage Mater., 2024, 70, 103470.
DOI: 10.1016/j.ensm.2024.103470

Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model

Kai Chen, Chuling Guo, Chaoping Wang, Shoushi Zhao, Beiyi Xiong, Guining Lu, John R. Reinfelder, Zhi Dang
Water Res., 2024, 256, 121580.
DOI: 10.1016/j.watres.2024.121580

Thermal transports of 2D phosphorous carbides by machine learning molecular dynamics simulations

Chenyang Cao, Shuo Cao, YuanXu Zhu, Haikuan Dong, Yanzhou Wang, Ping Qian
Int. J. Heat Mass Transf., 2024, 224, 125359.
DOI: 10.1016/j.ijheatmasstransfer.2024.125359

Unraveling medium-range order and melting mechanism of ZIF-4 under high temperature

Zuhao Shi, Bin Liu, Yuanzheng Yue, Arramel Arramel, Neng Li
J Am Ceram Soc., 2024, 107 (6), 3845–3856.
DOI: 10.1111/jace.19741

A deep-neural network potential to study transformation-induced plasticity in zirconia

Jin-Yu Zhang, Ga"el Huynh, Fu-Zhi Dai, Tristan Albaret, Shi-Hao Zhang, Shigenobu Ogata, David Rodney
J. Eur. Ceram. Soc., 2024, 44 (6), 4243–4254.
DOI: 10.1016/j.jeurceramsoc.2024.01.007

IR Spectroscopy of Carboxylate-Passivated Semiconducting Nanocrystals: Simulation and Experiment

Jakub K. Sowa, Danielle M. Cadena, Arshad Mehmood, Benjamin G. Levine, Sean T. Roberts, Peter J. Rossky
J. Phys. Chem. C, 2024, 128 (21), 8724–8731.
DOI: 10.1021/acs.jpcc.4c01988

Accelerating Computation of Acidity Constants and Redox Potentials for Aqueous Organic Redox Flow Batteries by Machine Learning Potential- Based Molecular Dynamics

Feng Wang, Zebing Ma, Jun Cheng
J. Am. Chem. Soc., 2024, 146 (21), 14566–14575.
DOI: 10.1021/jacs.4c01221

First-principles-based machine learning interatomic potential for molecular dynamics simulations of 2D lateral MoS2/WS2 heterostructures

Xiangjun Liu, Baolong Wang, Kun Jia, Quanjie Wang, Di Wang, Yucheng Xiong
J. Appl. Phys., 2024, 135 (20).
DOI: 10.1063/5.0201527

On-the-fly kinetic Monte Carlo simulations with neural network potentials for surface diffusion and reaction

Tomoko Yokaichiya, Tatsushi Ikeda, Koki Muraoka, Akira Nakayama
J. Chem. Phys., 2024, 160 (20), 204108.
DOI: 10.1063/5.0199240

Competing Reaction Mechanisms of Peptide Bond Formation in Water Revealed by Deep Potential Molecular Dynamics and Path Sampling

Rolf David, I\~naki Tu\~n'on, Damien Laage
J. Am. Chem. Soc., 2024, 146 (20), 14213–14224.
DOI: 10.1021/jacs.4c03445

Ultra-flat bands at large twist angles in group-V twisted bilayer materials

Zhi-Xiong Que, Shu-Zong Li, Bo Huang, Zhi-Xiong Yang, Wei- Bing Zhang
J. Chem. Phys., 2024, 160 (19), 194710.
DOI: 10.1063/5.0197757

Revealing the reconstruction mechanism of AgPd nanoalloys under fluorination based on a multiscale deep learning potential

Longfei Guo, Shuang Shan, Xiaoqing Liu, Wanxuan Zhang, Peng Xu, Fanzhe Ma, Zhen Li, Chongyang Wang, Junpeng Wang, Fuyi Chen
J. Chem. Phys., 2024, 160 (17), 174313.
DOI: 10.1063/5.0205616

Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials

Amir Omranpour, Pablo {Montero De Hijes}, J"org Behler, Christoph Dellago
J. Chem. Phys., 2024, 160 (17), 170901.
DOI: 10.1063/5.0201241

Exploring the Relationship between Composition and Li-Ion Conductivity in the Amorphous Li-La-Zr-O System

Dexin Zhang, Yiwei You, Fulun Wu, Xinrui Cao, Tie-Yu L"u, Yang Sun, Zi-Zhong Zhu, Shunqing Wu
Acs Mater. Lett,, 2024, 6 (5), 1849–1855.
DOI: 10.1021/acsmaterialslett.3c01558

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data

Zhiyu Shi, Aditya Dilip Lele, Ahren W. Jasper, Stephen J. Klippenstein, Yiguang Ju
J. Phys. Chem., A, 2024, 128 (17), 3449–3457.
DOI: 10.1021/acs.jpca.4c00750

Exploring Li-Ion Transport Properties of Li₃TiCl₆: A Machine Learning Molecular Dynamics Study

Selva Chandrasekaran Selvaraj, Volodymyr Koverga, Anh T. Ngo
J. Electrochem., Soc,, 2024, 171 (5), 50544.
DOI: 10.1149/1945-7111/ad4ac9

Deep-learning interatomic potential for iron at extreme conditions

Zhi Li, Sandro Scandolo
Phys. Rev. B, 2024, 109 (18), 184108.
DOI: 10.1103/PhysRevB.109.184108

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

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

Structural and mechanical properties of monolayer amorphous carbon and boron nitride

Xi Zhang, Yu-Tian Zhang, Yun-Peng Wang, Shiyu Li, Shixuan Du, Yu-Yang Zhang, Sokrates T. Pantelides
Phys. Rev. B, 2024, 109 (17), 174106.
DOI: 10.1103/PhysRevB.109.174106

Non-equilibrium nature of fracture determines the crack paths

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

Understanding cellulose pyrolysis via ab initio deep learning potential field

Yuqin Xiao, Yuxin Yan, Hainam Do, Richard Rankin, Haitao Zhao, Ping Qian, Keke Song, Tao Wu, Cheng Heng Pang
Bioresour. Technol., 2024, 399, 130590.
DOI: 10.1016/j.biortech.2024.130590

Lattice thermal conductivity and mechanical properties of the single- layer penta-NiN2 explored by a deep-learning interatomic potential

Pedram Mirchi, Christophe Adessi, Samy Merabia, Ali Rajabpour
Phys. Chem. Chem. Phys., 2024, 26 (19), 14216–14227.
DOI: 10.1039/d4cp00997e

Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials

Haikuan Dong, Yongbo Shi, Penghua Ying, Ke Xu, Ting Liang, Yanzhou Wang, Zezhu Zeng, Xin Wu, Wenjiang Zhou, Shiyun Xiong, Shunda Chen, Zheyong Fan
J. Appl. Phys., 2024, 135 (16).
DOI: 10.1063/5.0200833

Neural network molecular dynamics study of LiGe2(PO4)3: Investigation of structure

I.A. Balyakin, M.I. Vlasov, S.V. Pershina, D.M. Tsymbarenko, A.A. Rempel
Comput. Mater. Sci., 2024, 239, 112979.
DOI: 10.1016/j.commatsci.2024.112979

Validation workflow for machine learning interatomic potentials for complex ceramics

Kimia Ghaffari, Salil Bavdekar, Douglas E. Spearot, Ghatu Subhash
Comput. Mater. Sci., 2024, 239, 112983.
DOI: 10.1016/j.commatsci.2024.112983

Deep Potential fitting and mechanical properties study of MgAlSi alloy

Chang-sheng Zhu, Wen-jing Dong, Zi-hao Gao, Li-jun Wang, Guang-zhao Li
Comput. Mater. Sci., 2024, 239, 112966.
DOI: 10.1016/j.commatsci.2024.112966

Investigation of the Degradation of LiPF6- in Polar Solvents through Deep Potential Molecular Dynamics

Da Zhu, Li Sheng, Taiping Hu, Sian Chen, Mengchao Shi, Haiming Hua, Kai Yang, Jianlong Wang, Yaping Tang, Xiangming He, Hong Xu
J. Phys. Chem. Lett., 2024, 15 (15), 4024–4030.
DOI: 10.1021/acs.jpclett.4c00575

Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly

Fabian Zills, Moritz Ren'e Sch"afer, Nico Segreto, Johannes K"astner, Christian Holm, Samuel Tovey
J. Phys. Chem., B, 2024, 128 (15), 3662–3676.
DOI: 10.1021/acs.jpcb.3c07187

Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations

Rui Zhou, Kun Luo, Steve W. Martin, Qi An
Acs Appl. Mater. Interfaces, 2024, 16 (15), 18874–18887.
DOI: 10.1021/acsami.4c00618

Theoretical Study on Ion Diffusion Mechanism in W-Doped K3SbS4 as Solid-State Electrolyte for K-Ion Batteries

Rongyu Zhang, Shifeng Xu, Liyan Wang, Chuanyun Wang, Yongjun Zhou, Zhe L"u, Wenbo Li, Dan Xu, Sai Wang, Xu Yang
Inorg. Chem., 2024, 63 (15), 6743–6751.
DOI: 10.1021/acs.inorgchem.4c00074

Investigation of the effect of off-stoichiometric composition on oxygen transport in layered perovskite materials for SOFC cathode

Sung Hun Woo, Hyun Joo Yang, Yongseon Kim
Mater. Lett., 2024, 361, 136114.
DOI: 10.1016/j.matlet.2024.136114

Elucidating the local structure and properties of molten Na2CO3-K2CO3 salts using Machine Learning-Driven molecular dynamics

Taixi Feng, Bo Yang, Jia Zhao, Guimin Lu
Chem. Eng. Sci., 2024, 288, 119836.
DOI: 10.1016/j.ces.2024.119836

Development of machine learning force field for thermal conductivity analysis in MoAlB: Insights into anisotropic heat transfer mechanisms

Hanchao Zhang, Guoliang Ren, Peng Jia, Xiaofeng Zhao, Na Ni
Ceram. Int., 2024, 50 (8), 13740–13749.
DOI: 10.1016/j.ceramint.2024.01.288

Training machine learning potentials for reactive systems: A Colab tutorial on basic models

Xiaoliang Pan, Ryan Snyder, Jia-Ning Wang, Chance Lander, Carly Wickizer, Richard Van, Andrew Chesney, Yuanfei Xue, Yuezhi Mao, Ye Mei, Jingzhi Pu, Yihan Shao
J. Comput. Chem., 2024, 45 (10), 638–647.
DOI: 10.1002/jcc.27269

Unified deep learning network for enhanced accuracy in predicting thermal conductivity of bilayer graphene, hexagonal boron nitride, and their heterostructures

Rongkun Chen, Yu Tian, Jiayi Cao, Weina Ren, Shiqian Hu, Chunhua Zeng
J. Appl. Phys., 2024, 135 (14).
DOI: 10.1063/5.0201698

Many-body interactions and deep neural network potentials for water

Yaoguang Zhai, Richa Rashmi, Etienne Palos, Francesco Paesani
J. Chem. Phys., 2024, 160 (14), 144501.
DOI: 10.1063/5.0203682

Transferable Water Potentials Using Equivariant Neural Networks

Tristan Maxson, Tibor Szilv'asi
J. Phys. Chem. Lett., 2024, 15 (14), 3740–3747.
DOI: 10.1021/acs.jpclett.4c00605

Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics

Takeru Miyagawa, Namita Krishnan, Manuel Grumet, Christian Rever'on Baecker, Waldemar Kaiser, David A. Egger
J. Mater. Chem. A, 2024, 12 (19), 11344–11361.
DOI: 10.1039/d4ta00452c

Effect of Interlayer Bonding on Superlubric Sliding of Graphene Contacts: A Machine-Learning Potential Study

Penghua Ying, Amir Natan, Oded Hod, Michael Urbakh
Acs Nano, 2024, 18 (14), 10133–10141.
DOI: 10.1021/acsnano.3c13099

Prediction of phonon properties of cubic boron nitride with vacancy defects and isotopic disorders by using a neural network potential

Jingwen Zhang, Junjie Zhang, Guoqiang Bao, Zehan Li, Xiaobo Li, Te-Huan Liu, Ronggui Yang
Appl. Phys. Lett., 2024, 124 (15).
DOI: 10.1063/5.0198431

Hydrocarbon Species on the Cu(111) Surface Studied with a Neural Network Potential

Sen Xu, Liling Wu, Yi Fan, Yufeng Liu, Xiongzhi Zeng, Zhenyu Li
J. Phys. Chem. C, 2024, 128 (13), 5697–5707.
DOI: 10.1021/acs.jpcc.3c08138

Long-range proton and hydroxide ion transfer dynamics at the water/CeO2 interface in the nanosecond regime: reactive molecular dynamics simulations and kinetic analysis

Taro Kobayashi, Tatsushi Ikeda, Akira Nakayama
Chem. Sci., 2024, 15 (18), 6816–6832.
DOI: 10.1039/d4sc01422g

Exploring diffusion behavior of superionic materials using machine- learning interatomic potentials

Cheng-Rong Hsing, Duc-Long Nguyen, Ching-Ming Wei
Phys, Rev, Mater., 2024, 8 (4), 43806.
DOI: 10.1103/PhysRevMaterials.8.043806

Photocatalytic activity of dual defect modified graphitic carbon nitride is robust to tautomerism: machine learning assisted ab initio quantum dynamics

Sraddha Agrawal, Bipeng Wang, Yifan Wu, David Casanova, Oleg V. Prezhdo
Nanoscale, 2024, 16 (18), 8986–8995.
DOI: 10.1039/d4nr00606b

Hydrogen Diffusion in the Lower Mantle Revealed by Machine Learning Potentials

Yihang Peng, Jie Deng
Jgr Solid Earth, 2024, 129 (4).
DOI: 10.1029/2023JB028333

Deciphering the controlling factors for phase transitions in zeolitic imidazolate frameworks

Tao Du, Shanwu Li, Sudheer Ganisetti, Mathieu Bauchy, Yuanzheng Yue, Morten M. Smedskjaer
Natl. Sci. Rev., 2024, 11 (4), nwae023.
DOI: 10.1093/nsr/nwae023

Machine learning heralding a new development phase in molecular dynamics simulations

Eva Pra\vsnikar, Martin Ljubi\vc, Andrej Perdih, Jure Bori\vsek
Artif Intell Rev, 2024, 57 (4), 102.
DOI: 10.1007/s10462-024-10731-4

Machine learning-based prediction of mechanical properties of N-doped $\gamma$-graphdiyne

Cun Zhang, Bolin Yang, Zhilong Peng, Shaohua Chen
Sci. China Mater., 2024, 67 (4), 1129–1139.
DOI: 10.1007/s40843-023-2733-7

Determining the thermal conductivity and phonon behavior of SiC materials with quantum accuracy via deep learning interatomic potential model

Baoqin Fu, Yandong Sun, Wanrun Jiang, Fu Wang, Linfeng Zhang, Han Wang, Ben Xu
J. Nucl. Mater., 2024, 591, 154897.
DOI: 10.1016/j.jnucmat.2024.154897

A 3D metallic porous sulfurized carbon anode identified by global structure search for Na-ion batteries with fast diffusion kinetics

Mohammed M. Obeid, Jiahui Liu, Penghu Du, Tongyu Liu, Qiang Sun
J. Energy Storage, 2024, 82, 110587.
DOI: 10.1016/j.est.2024.110587

Probing the critical point of MgSiO3 using deep potential simulation

Fei-Yang Xu, Zhi-Guo Li, Xiang-Rong Chen, Hua Y. Geng, Lei Liu, Jianbo Hu
J. Appl. Phys., 2024, 135 (12).
DOI: 10.1063/5.0189696

Ab Initio Driven Exploration on the Thermal Properties of Al-Li Alloy

Xiaoya Chang, Yongchao Wu, Qingzhao Chu, Gang Zhang, Dongping Chen
Acs Appl. Mater. Interfaces, 2024, 16 (12), 14954–14964.
DOI: 10.1021/acsami.4c01480

Compositional transferability of deep potential in molten LiF-BeF2 and LaF3 mixtures: prediction of density, viscosity, and local structure

Xuejiao Li, Tingrui Xu, Yu Gong
Phys. Chem. Chem. Phys., 2024, 26 (15), 12044–12052.
DOI: 10.1039/d4cp00079j

Compression Eliminates Charge Traps by Stabilizing Perovskite Grain Boundary Structures: An Ab Initio Analysis with Machine Learning Force Field

Dongyu Liu, Yifan Wu, Mikhail R. Samatov, Andrey S. Vasenko, Evgueni V. Chulkov, Oleg V. Prezhdo
Chem. Mater.: Publ. Am. Chem. Soc., 2024, 36 (6), 2898–2906.
DOI: 10.1021/acs.chemmater.3c03261

Neural Network-Based Sum-Frequency Generation Spectra of Pure and Acidified Water Interfaces with Air

Miguel {de la Puente}, Axel Gomez, Damien Laage
J. Phys. Chem. Lett., 2024, 15 (11), 3096–3102.
DOI: 10.1021/acs.jpclett.4c00113

A neural network potential energy surface assisted molecular dynamics study on the pyrolysis behavior of two spiro-hydrocarbons

Hang Xiao, Bin Yang
Phys. Chem. Chem. Phys., 2024, 26 (15), 11867–11879.
DOI: 10.1039/d3cp05425j

Thermal Conductivity of MgSiO_{3\ens uremath}- H₂O System Determined by Machine Learning Potentials

Yihang Peng, Jie Deng
Geophys. Res. Lett., 2024, 51 (5).
DOI: 10.1029/2023GL107245

The thermal decomposition mechanism of RDX/AP composites: ab initio neural network MD simulations

Kehui Pang, Mingjie Wen, Xiaoya Chang, Yabei Xu, Qingzhao Chu, Dongping Chen
Phys. Chem. Chem. Phys., 2024, 26 (15), 11545–11557.
DOI: 10.1039/d3cp05709g

Electrical conductivity of copper under ultrahigh pressure and temperature conditions by both experiments and first-principles simulations

Bo Gan, Jun Li, Junjie Gao, Qiru Zeng, Wenhao Song, Yukai Zhuang, Yingxin Hua, Qiang Wu, Gang Jiang, Yuan Yin, Youjun Zhang
Phys. Rev. B, 2024, 109 (11), 115129.
DOI: 10.1103/PhysRevB.109.115129

A reactive molecular dynamics model for uranium/hydrogen containing systems

Artem Soshnikov, Rebecca Lindsey, Ambarish Kulkarni, Nir Goldman
J. Chem. Phys., 2024, 160 (9), 94117.
DOI: 10.1063/5.0183610

Deep Potential Molecular Dynamics Study of Propane Oxidative Dehydrogenation

Ziyi Liu, An-Hui Lu, Dongqi Wang
J. Phys. Chem., A, 2024, 128 (9), 1656–1664.
DOI: 10.1021/acs.jpca.3c07859

Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage

Joshua Ojih, Mohammed Al-Fahdi, Yagang Yao, Jianjun Hu, Ming Hu
J. Mater. Chem. A, 2024, 12 (14), 8502–8515.
DOI: 10.1039/d3ta06190f

Visualizing the SEI formation between lithium metal and solid-state electrolyte

Fucheng Ren, Yuqi Wu, Wenhua Zuo, Wengao Zhao, Siyuan Pan, Hongxin Lin, Haichuan Yu, Jing Lin, Min Lin, Xiayin Yao, Torsten Brezesinski, Zhengliang Gong, Yong Yang
Energy Env., Sci,, 2024, 17 (8), 2743–2752.
DOI: 10.1039/d3ee03536k

Machine learning potential for modelling H2 adsorption/diffusion in MOFs with open metal sites

Shanping Liu, Romain Dupuis, Dong Fan, Salma Benzaria, Mickaele Bonneau, Prashant Bhatt, Mohamed Eddaoudi, Guillaume Maurin
Chem. Sci., 2024, 15 (14), 5294–5302.
DOI: 10.1039/d3sc05612k

Modifying ring structures in lithium borate glasses under compression: MD simulations using a machine-learning potential

Shingo Urata, Aik Rui Tan, Rafael G'omez-Bombarelli
Phys, Rev, Mater., 2024, 8 (3), 33602.
DOI: 10.1103/PhysRevMaterials.8.033602

Thermoelastic properties of bridgmanite using deep-potential molecular dynamics

Tianqi Wan, Chenxing Luo, Yang Sun, Renata M. Wentzcovitch
Phys. Rev. B, 2024, 109 (9), 94101.
DOI: 10.1103/PhysRevB.109.094101

Understanding the dielectric relaxation of liquid water using neural network potential and classical pairwise potential

Jae Hyun Ryu, Ji Woong Yu, Tae Jun Yoon, Won Bo Lee
J. Mol. Liq., 2024, 397, 124054.
DOI: 10.1016/j.molliq.2024.124054

Synthetic pre-training for neural-network interatomic potentials

John L A Gardner, Kathryn T Baker, Volker L Deringer
Mach, Learn.,: Sci, Technol,, 2024, 5 (1), 15003.
DOI: 10.1088/2632-2153/ad1626

Review of deep learning algorithms in molecular simulations and perspective applications on petroleum engineering

Jie Liu, Tao Zhang, Shuyu Sun
Geosci. Front., 2024, 15 (2), 101735.
DOI: 10.1016/j.gsf.2023.101735

Machine-learned atomic cluster expansion potentials for fast and quantum-accurate thermal simulations of wurtzite AlN

Guang Yang, Yuan-Bin Liu, Lei Yang, Bing-Yang Cao
Arxiv, 2024, 135 (8).
DOI: 10.1063/5.0188905

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DP/MM: A Hybrid Model for Zinc-Protein Interactions in Molecular Dynamics

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Molecular Dynamics Study of Silicon Carbide Using an Ab Initio-Based Neural Network Potential: Effect of Composition and Temperature on Crystallization Behavior

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Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations

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Modeling Exchange Reactions in Covalent Adaptable Networks with Machine Learning Force Fields

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Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

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Anomalous Thermal Transport across the Superionic Transition in Ice

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

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Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

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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

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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.
DOI: 10.1103/PhysRevB.103.024108

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.
DOI: 10.1063/5.0031215

2020

Simulating Diffusion Properties of Solid-State Electrolytes via a Neural Network Potential: Performance and Training Scheme

Aris Marcolongo, Tobias Binninger, Federico Zipoli, Teodoro Laino
Chemsystemschem, 2020, 2 (3).
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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.
DOI: 10.1039/C9SC05116C

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.
DOI: 10.1063/5.0025051

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.
DOI: 10.1088/1361-648X/ab5890

Ab initio phase diagram and nucleation of gallium

Haiyang Niu, Luigi Bonati, Pablo M Piaggi, Michele Parrinello
Nat. Commun., 2020, 11, 2654.
DOI: 10.1038/s41467-020-16372-9

Raman spectrum and polarizability of liquid water from deep neural networks

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

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.
DOI: 10.1063/5.0001491

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.
DOI: 10.1039/D0SM01019G

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.
DOI: 10.1103/PhysRevB.102.041121

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

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

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.
DOI: 10.1103/PhysRevB.102.214113

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.
DOI: 10.1021/acs.jpclett.0c02547

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.
DOI: 10.1016/j.mtphys.2020.100181

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.
DOI: 10.1103/PhysRevB.102.115155

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.
DOI: 10.1016/j.jeurceramsoc.2020.06.007

Crystal Structure Prediction of Binary Alloys via Deep Potential

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

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.
DOI: 10.1103/PhysRevMaterials.4.113803

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.
DOI: 10.1103/PhysRevMaterials.3.023804

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.
DOI: 10.1080/00268976.2019.1652366

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.
DOI: 10.1103/PhysRevB.100.174101

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.
DOI: 10.1063/1.5098061

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.
DOI: 10.1063/1.5027645

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.
DOI: 10.1103/PhysRevLett.120.143001