Vacancy-induced phonon localization and lattice softening for reduced thermal conductivity in Mg3Sb2

Sheng Zhang, Jinbao Zhang, Liqi Ren, Yijing Shao, Wanyue Yan, Meng Tian, Kunling Peng, Ping Lin and Yunzhen Du

Phys. Chem. Chem. Phys., 2026, 28, 565
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Multiscale simulation study on the mechanical, electrical, and thermal properties of ZnSb semiconductor

Shihui Chen, Hanwen Yang, Guang Yang, Minghui Cheng, Zipei Zhang, Zhangfeng Wu and Jianping Lin

J. Mater. Chem. C, 2025, 13, 17692
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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 and Rui Xiong

Phys. Chem. Chem. Phys., 2023, 25, 15422
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Impact of vacancy defects on the thermal conductivity of BaAgBi: a comprehensive study using molecular dynamics simulations with neural network potentials

Yunzhen Du, Yuan Yao, Kunling Peng, Jizheng Duan, Changwei Hao, Yuan Tian, Wenshan Duan, Lei Yang, Ping Lin and Sheng Zhang

Phys. Chem. Chem. Phys., 2024, 26, 24342
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Exploring the phase change and structure of carbon using a deep learning interatomic potential

Kai Chen, Riyi Yang, Zhefeng Wang, Wuyan Zhao, Youmin Xu, Huaijun Sun, Chao Zhang, Songyou Wang, Kaiming Ho, Cai-Zhuang Wang and Wan-Sheng Su

Phys. Chem. Chem. Phys., 2024, 26, 25936
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Predicting melting temperatures across the periodic table with machine learning atomistic potentials

Christopher M. Andolina and Wissam A. Saidi

Digital Discovery, 2024, 3, 1421
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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 and Ali Rajabpour

Phys. Chem. Chem. Phys., 2024, 26, 14216
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Optimization of RF to alloy elastic modulus prediction based on cuckoo algorithm

Chang-sheng Zhu, Guang-zhao Li, Naranjo Villota Jose Luis, Wen-jing Dong and Li-jun Wang

Computational Materials Science, 2024, 231, 112515
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Neural network potential model molecular dynamics study on the effect of temperature and pressure on the lattice thermal conductivity of Mg3Sb2

Jiaojiao Cheng, Cunjie Duan, Yunzhen Du, Jizheng Duan, Meiling Qi, Yanwei Chen, Lei Yang, Wenshan Duan, Sheng Zhang and Ping Lin

J Mater Sci, 2025, 60, 6551
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NVNMD-v2: Scalable and Accurate Deep Learning Molecular Dynamics Model Based on Non-Von Neumann Architectures

Xiaoyun Yu, Guang Yang, Zhuoying Zhao, Junhua Li, Xinyu Xiao, Xin Zhang, Jie Liu and Pinghui Mo

J. Chem. Theory Comput., 2025, 21, 8918
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Thermal conductivity predictions in monolayer MoSi2N4: Integrating neural network potentials with phonon scattering analysis

Yunzhen Du, Jiaojiao Cheng, Jizheng Duan, Meiling Qi, Yanwei Chen, Yuan Yao, Wenshan Duan, Lei Yang, Sheng Zhang and Ping Lin

Computational Materials Science, 2025, 247, 113543
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Thermal conductivity in MoSi₂N₄(MoN)ₙ: Insights into phonon scattering and transport

Yunzhen Du, Kunling Peng, Jizheng Duan, Meiling Qi, Yanwei Chen, Changwei Hao, Wenshan Duan, Lei Yang, Sheng Zhang and Ping Lin

International Communications in Heat and Mass Transfer, 2024, 159, 108361
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Exploring the strength-concentration relationship of MgCu bioalloys at low copper content using machine learning force fields

Xingze Geng, Lin-Wang Wang and Xiangying Meng

Computational Materials Science, 2025, 246, 113486
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Application and acceleration of machine learning potential construction using farthest point sampling: A case study of carbon nanotubes

Changhong Zhang, Jie Liang, Yi Li, Rongmei Chen, Yuanhao Miao, Zhenyu Wang, Lei Zhang, Haoyuan Li and Hanshen Xin

Diamond and Related Materials, 2025, 158, 112563
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TiAlNb alloy interatomic potentials: comparing passive and active machine learning techniques with MTP and DeePMD

Anju Chandran, Archa Santhosh, Claudio Pistidda, Paul Jerabek, Roland C. Aydin and Christian J. Cyron

Front. Mater., 2025, 12
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Comparative analysis of ternary TiAlNb interatomic potentials: moment tensor vs. deep learning approaches

Anju Chandran, Archa Santhosh, Claudio Pistidda, Paul Jerabek, Roland C. Aydin and Christian J. Cyron

Front. Mater., 2024, 11
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Molecular dynamics simulations of the shear and tensile mechanical properties of rare-earth metal erbium based on deep-learning potential

Hong-Tao Xue, Zhen Chang, Juan Li, Yan-Hong Yang, Jun-Qiang Ren, Yong Zhang, Fu-Ling Tang, Xue-Feng Lu and Jun-Chen Li

Materials Today Communications, 2024, 41, 110485
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Deep Learning-Enhanced Titanium Potential Models for Accurate Molecular Dynamics

Yongzhi Wu, Haoxiang Zhang, Hanqing Li, Keyuan Chen, Jinlong Chen, Xingkao Zhang, Ju Rong, Yudong Sui, Xiaohua Yu and Jing Feng

J. Phys. Chem. C, 2025, 129, 9066
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Application of Deep-Learning Potential in Simulating the Structural and Physical Characteristics of Platinum

Keyuan Chen, Xingkao Zhang, Li Ma, Jueyi Ye, Qi Qiu, Haoxiang Zhang, Ju Rong, Yudong Sui, Xiaohua Yu and Jing Feng

CMC, 2025, 83, 685
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Deep-learning potential molecular dynamics simulations of the structural and physical properties of rare-earth metal scandium

Hong-Tao Xue, Juan Li, Zhen Chang, Yan-Hong Yang, Fu-Ling Tang, Yong Zhang, Jun-Qiang Ren, Xue-Feng Lu and Jun-Chen Li

Computational Materials Science, 2024, 242, 113072
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A machine learning potential for simulation the dislocation behavior of magnesium

Jincheng Kan, Zhigang Ding, Xiang Chen, Huaiyu Hou, Yonghao Zhao and Wei Liu

Journal of Magnesium and Alloys, 2026, 16, 101593
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Neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia

Sebastian Bichelmaier, Jesús Carrete, Ralf Wanzenböck, Florian Buchner and Georg K. H. Madsen

Phys. Rev. B, 2023, 107
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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 and Fubo Tian

Computational Materials Science, 2024, 244, 113154
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Origin of the machine learning forces field errors across metal elements

Xingze Geng, Wentao Zhang, Lin-Wang Wang and Xiangying Meng

npj Comput Mater, 2026, 12
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A comprehensive investigation on the accuracy and efficiency of methods for melting temperature calculation using molecular dynamics simulations

Xinwei Wang, Mengxin Yang, Xiaoqian Gai, Yibo Sun, Bohan Cao, Jiajin Chen, Min Liang, Fubo Tian and Liang Li

Journal of Molecular Liquids, 2024, 395, 123924
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Exploring the Coexistence Conditions and Intrinsic Relationships between Electrides, Superconductivity, Kagome Lattice, and Superionic Behaviors in Li–Ge Compounds

Xinwei Wang, Wenting Tang, Xiao-Wei Sun, Zi-Jiang Liu, Bohan Cao, Mengxin Yang, Yibo Sun, Tian Cui, Liang Li and Fubo Tian

ACS Materials Lett., 2024, 6, 3697
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Review of deep learning algorithms in molecular simulations and perspective applications on petroleum engineering

Jie Liu, Tao Zhang and Shuyu Sun

Geoscience Frontiers, 2024, 15, 101735
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Construction of a neural network potential for SiC and its application in uniaxial tension simulations

Yunzhen Du, Changwei Hao, Zhaocang Meng, Canglong Wang, Kunling Peng, Yuan Tian, Wenshan Duan, Lei Yang, Ping Lin and Sheng Zhang

Computational Materials Science, 2024, 242, 113078
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