Publications

2024

  1. Ethan Berger and Hannu-Pekka Komsa. Polarizability models for simulations of finite temperature raman spectra from machine learning molecular dynamics. Phys. Rev. Mater., 8:043802, Apr 2024. URL: https://link.aps.org/doi/10.1103/PhysRevMaterials.8.043802, doi:10.1103/PhysRevMaterials.8.043802.

  2. Chenyang Cao, Shuo Cao, YuanXu Zhu, Haikuan Dong, Yanzhou Wang, and Ping Qian. Thermal transports of 2d phosphorous carbides by machine learning molecular dynamics simulations. International Journal of Heat and Mass Transfer, 224:125359, 2024. doi:10.1016/j.ijheatmasstransfer.2024.125359.

  3. Rongkun Chen, Yu Tian, Jiayi Cao, Weina Ren, Shiqian Hu, and Chunhua Zeng. Unified deep learning network for enhanced accuracy in predicting thermal conductivity of bilayer graphene, hexagonal boron nitride, and their heterostructures. Journal of Applied Physics, 135(14):145106, 04 2024. URL: https://doi.org/10.1063/5.0201698, doi:10.1063/5.0201698.

  4. Shunda Chen, Xiaochen Jin, Wanyu Zhao, and Tianshu Li. Intricate short-range order in gesn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials. Phys. Rev. Mater., 8:043805, Apr 2024. doi:10.1103/PhysRevMaterials.8.043805.

  5. Zekun Chen, Margaret L. Berrens, Kam-Tung Chan, Zheyong Fan, and Davide Donadio. Thermodynamics of water and ice from a fast and scalable first-principles neuroevolution potential. Journal of Chemical & Engineering Data, 69(1):128–140, 2024. doi:10.1021/acs.jced.3c00561.

  6. Ruihuan Cheng, Zezhu Zeng, Chen Wang, Niuchang Ouyang, and Yue Chen. Impact of strain-insensitive low-frequency phonon modes on lattice thermal transport in $\mathrm A_2x\mathrm B_6$-type perovskites. Phys. Rev. B, 109:054305, Feb 2024. doi:10.1103/PhysRevB.109.054305.

  7. Haikuan Dong, Yongbo Shi, Penghua Ying, Ke Xu, Ting Liang, Yanzhou Wang, Zezhu Zeng, Xin Wu, Wenjiang Zhou, Shiyun Xiong, Shunda Chen, and Zheyong Fan. Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials. Journal of Applied Physics, 135(16):161101, 04 2024. doi:10.1063/5.0200833.

  8. Hongzhao Fan, Penghua Ying, Zheyong Fan, Yue Chen, Zhigang Li, and Yanguang Zhou. Anomalous strain-dependent thermal conductivity in the metal-organic framework hkust-1. Phys. Rev. B, 109:045424, Jan 2024. doi:10.1103/PhysRevB.109.045424.

  9. Zheyong Fan, Yang Xiao, Yanzhou Wang, Penghua Ying, Shunda Chen, and Haikuan Dong. Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials. Journal of Physics: Condensed Matter, 36(24):245901, mar 2024. doi:10.1088/1361-648X/ad31c2.

  10. Mandi Fang, Shi Tang, Zheyong Fan, Yao Shi, Nan Xu, and Yi He. Transferability of machine learning models for predicting raman spectra. The Journal of Physical Chemistry A, 128(12):2286–2294, 2024. doi:10.1021/acs.jpca.3c07109.

  11. Guan Huang, Lichuan Zhang, Shibing Chu, Yuee Xie, and Yuanping Chen. A highly ductile carbon material made of triangle rings: a study of machine learning. Applied Physics Letters, 124(4):043103, 01 2024. doi:10.1063/5.0189906.

  12. Xiaofan Huang, Chengzhi Li, Minhui Yuan, Jing Shuai, Xiang-Guo Li, and Yanglong Hou. Unphysical grain size dependence of lattice thermal conductivity in mg3(sb, bi)2: an atomistic view of concentration dependent segregation effects. Materials Today Physics, pages 101386, 2024. doi:https://doi.org/10.1016/j.mtphys.2024.101386.

  13. Guotai Li, Jialin Tang, Jiongzhi Zheng, Qi Wang, Zheng Cui, Ke Xu, Jianbin Xu, Te-Huan Liu, Guimei Zhu, Ruiqiang Guo, and Baowen Li. Convergent thermal conductivity in strained monolayer graphene. Phys. Rev. B, 109:035420, Jan 2024. URL: https://link.aps.org/doi/10.1103/PhysRevB.109.035420, doi:10.1103/PhysRevB.109.035420.

  14. Kaiqi Li, Bin Liu, Jian Zhou, and Zhimei Sun. Revealing the crystallization dynamics of sb–te phase change materials by large-scale simulations. J. Mater. Chem. C, pages –, 2024. doi:10.1039/D3TC04586B.

  15. Youtian Li, Yangyu Guo, Shiyun Xiong, and Hongliang Yi. Enhanced heat transport in amorphous silicon via microstructure modulation. International Journal of Heat and Mass Transfer, 222:125167, 2024. doi:10.1016/j.ijheatmasstransfer.2023.125167.

  16. Zhiqiang Li, Haoyu Dong, Jian Wang, Linhua Liu, and Jia-Yue Yang. Active learning molecular dynamics-assisted insights into ultralow thermal conductivity of two-dimensional covalent organic frameworks. International Journal of Heat and Mass Transfer, 225:125404, 2024. doi:https://doi.org/10.1016/j.ijheatmasstransfer.2024.125404.

  17. Paolo Pegolo and Federico Grasselli. Thermal transport of glasses via machine learning driven simulations. Frontiers in Materials, 2024. doi:10.3389/fmats.2024.1369034.

  18. Zijun Qi, Xiang Sun, Zhanpeng Sun, Qijun Wang, Dongliang Zhang, Kang Liang, Rui Li, Diwei Zou, Lijie Li, Gai Wu, Wei Shen, and Sheng Liu. Interfacial optimization for aln/diamond heterostructures via machine learning potential molecular dynamics investigation of the mechanical properties. ACS Applied Materials & Interfaces, 16(21):27998–28007, 2024. doi:10.1021/acsami.4c06055.

  19. Christian Schäfer, Jakub Fojt, Eric Lindgren, and Paul Erhart. Machine learning for polaritonic chemistry: accessing chemical kinetics. Journal of the American Chemical Society, 146(8):5402–5413, 2024. doi:10.1021/jacs.3c12829.

  20. Chenghan Sun, Rajat Goel, and Ambarish R. Kulkarni. Developing cheap but useful machine learning-based models for investigating high-entropy alloy catalysts. Langmuir, 40(7):3691–3701, 2024. doi:10.1021/acs.langmuir.3c03401.

  21. Jialin Tang, Jiongzhi Zheng, Xiaohan Song, Lin Cheng, and Ruiqiang Guo. In-plane thermal conductivity of hexagonal boron nitride from 2d to 3d. Journal of Applied Physics, 135(20):205105, 05 2024. doi:10.1063/5.0206028.

  22. Heqing Tian, Wenhao Dong, Wenguang Zhang, and Chaxiu Guo. Machine learning techniques to probe the properties of molten salt phase change materials for thermal energy storage. Cell Reports Physical Science, pages 102042, 2024. doi:https://doi.org/10.1016/j.xcrp.2024.102042.

  23. Bing Wang, Penghua Ying, and Jin Zhang. The thermoelastic properties of monolayer covalent organic frameworks studied by machine-learning molecular dynamics. Nanoscale, 16(1):237–248, 2024. doi:10.1039/D3NR04509A.

  24. Xiaonan Wang, Jinfeng Yang, Penghua Ying, Zheyong Fan, Jin Zhang, and Huarui Sun. Dissimilar thermal transport properties in κ-ga2o3 and β-ga2o3 revealed by homogeneous nonequilibrium molecular dynamics simulations using machine-learned potentials. Journal of Applied Physics, 135(6):065104, 2024. doi:10.1063/5.0185854.

  25. Zhang Wu, Rumeng Liu, Ning Wei, and Lifeng Wang. Unexpected reduction in thermal conductivity observed in graphene/h-bn heterostructures. Physical Chemistry Chemical Physics, 26(5):3823–3831, 2024. doi:10.1039/D3CP05407A.

  26. Nan Xu, Petter Rosander, Christian Schäfer, Eric Lindgren, Nicklas Österbacka, Mandi Fang, Wei Chen, Yi He, Zheyong Fan, and Paul Erhart. Tensorial properties via the neuroevolution potential framework: fast simulation of infrared and raman spectra. Journal of Chemical Theory and Computation, 20(8):3273–3284, 2024. doi:10.1021/acs.jctc.3c01343.

  27. Penghua Ying, Amir Natan, Oded Hod, and Michael Urbakh. Effect of interlayer bonding on superlubric sliding of graphene contacts: a machine-learning potential study. ACS Nano, 18(14):10133–10141, 2024. doi:10.1021/acsnano.3c13099.

  28. Maolin Yu, Zhiqiang Zhao, Wanlin Guo, and Zhuhua Zhang. Fracture toughness of two-dimensional materials dominated by edge energy anisotropy. Journal of the Mechanics and Physics of Solids, 186:105579, 2024. doi:10.1016/j.jmps.2024.105579.

  29. Jincheng Yue, Shiqian Hu, Bin Xu, Rongkun Chen, Long Xiong, Rulei Guo, Yuanzhe Li, Lei-Lei Nian, Junichiro Shiomi, and Bo Zheng. Unraveling the mechanisms of thermal boundary conductance at the graphene-silicon interface: insights from ballistic, diffusive, and localized phonon transport regimes. Phys. Rev. B, 109:115302, Mar 2024. doi:10.1103/PhysRevB.109.115302.

  30. Majid Zeraati, Artem R. Oganov, Tao Fan, and Sergey F. Solodovnikov. Searching for low thermal conductivity materials for thermal barrier coatings: a theoretical approach. Phys. Rev. Mater., 8:033601, 2024. doi:10.1103/PhysRevMaterials.8.033601.

  31. Jian Zhang, Hao-Chun Zhang, Weifeng Li, and Gang Zhang. Thermal conductivity of gete crystals based on machine learning potentials. Chinese Physics B, 2024. doi:10.1088/1674-1056/ad1b42.

2023

  1. EA Bea, A Mancardo Viotti, MF Carusela, AG Monastra, and A Soba. Assessment, improvement, and comparison of different computational tools used for the simulation of heat transport in nanostructures. SIMULATION, 99(3):237–244, 2023. doi:10.1177/00375497211009611.

  2. Ruihuan Cheng, Xingchen Shen, Stefan Klotz, Zezhu Zeng, Zehua Li, Alexandre Ivanov, Yu Xiao, Li-Dong Zhao, Frank Weber, and Yue Chen. Lattice dynamics and thermal transport of pbte under high pressure. Phys. Rev. B, 108:104306, 2023. doi:10.1103/PhysRevB.108.104306.

  3. Yajuan Cheng, Zheyong Fan, Tao Zhang, Masahiro Nomura, Sebastian Volz, Guimei Zhu, Baowen Li, and Shiyun Xiong. Magic angle in thermal conductivity of twisted bilayer graphene. Materials Today Physics, 35:101093, 2023. doi:10.1016/j.mtphys.2023.101093.

  4. Insa F. de Vries, Helena Osthues, and Nikos L. Doltsinis. Thermal conductivity across transition metal dichalcogenide bilayers. iScience, 2023. doi:10.1016/j.isci.2023.106447.

  5. Haikuan Dong, Chenyang Cao, Penghua Ying, Zheyong Fan, Ping Qian, and Yanjing Su. Anisotropic and high thermal conductivity in monolayer quasi-hexagonal fullerene: A comparative study against bulk phase fullerene. International Journal of Heat and Mass Transfer, 206:123943, 2023. doi:10.1016/j.ijheatmasstransfer.2023.123943.

  6. Peng-Hu Du, Cunzhi Zhang, Tingwei Li, and Qiang Sun. Low lattice thermal conductivity with two-channel thermal transport in the superatomic crystal PH₄AlBr₄. Physical Review B, 107(15):155204, 2023. doi:10.1103/PhysRevB.107.155204.

  7. Fredrik Eriksson, Erik Fransson, Christopher Linderälv, Zheyong Fan, and Paul Erhart. Tuning the through-plane lattice thermal conductivity in van der waals structures through rotational (dis)ordering. ACS Nano, 17:25565, 2023. doi:10.1021/acsnano.3c09717.

  8. Erik Fransson, J. Magnus Rahm, Julia Wiktor, and Paul Erhart. Revealing the free energy landscape of halide perovskites: metastability and transition characters in cspbbr3 and mapbi3. Chemistry of Materials, 35:8229–8238, 2023. doi:10.1021/acs.chemmater.3c01740.

  9. Erik Fransson, Petter Rosander, Fredrik Eriksson, J. Magnus Rahm, Terumasa Tadano, and Paul Erhart. Limits of the phonon quasi-particle picture at the cubic-to-tetragonal phase transition in halide perovskites. Commun Phys, 6:173, 2023. doi:10.1038/s42005-023-01297-8.

  10. Erik Fransson, Julia Wiktor, and Paul Erhart. Phase transitions in inorganic halide perovskites from machine-learned potentials. The Journal of Physical Chemistry C, 127:13773–13781, 2023. doi:10.1021/acs.jpcc.3c01542.

  11. Yu Li and Jin-Wu Jiang. Vacancy defects impede the transition from peapods to diamond: a neuroevolution machine learning study. Phys. Chem. Chem. Phys., 25:25629, 2023. doi:10.1039/D3CP03862A.

  12. Ting Liang, Penghua Ying, Ke Xu, Zhenqiang Ye, Chao Ling, Zheyong Fan, and Jianbin Xu. Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics. Phys. Rev. B, 108:184203, Nov 2023. doi:10.1103/PhysRevB.108.184203.

  13. Jiahui Liu, Jesper Byggmästar, Zheyong Fan, Ping Qian, and Yanjing Su. Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten. Phys. Rev. B, 108:054312, 2023. doi:10.1103/PhysRevB.108.054312.

  14. Yingzhou Liu, Yinong Liu, Jincheng Yue, Long Xiong, Lei-Lei Nian, and Shiqian Hu. Modulation of interface modes for resonance-induced enhancement of the interfacial thermal conductance in pillar-based si/ge nanowires. Phys. Rev. B, 108:235426, Dec 2023. URL: https://link.aps.org/doi/10.1103/PhysRevB.108.235426, doi:10.1103/PhysRevB.108.235426.

  15. Chenchen Lu, Zhi-hui Li, Shanchen Li, Zhen Li, Yingyan Zhang, Junhua Zhao, and Ning Wei. Molecular dynamics study of thermal transport properties across covalently bonded graphite-nanodiamond interfaces. Carbon, 213:118250, 2023. doi:10.1016/j.carbon.2023.118250.

  16. Yimu Lu, Yongbo Shi, Junyuan Wang, Haikuan Dong, and Jie Yu. Reduction of thermal conductivity in carbon nanotubes by fullerene encapsulation from machine-learning molecular dynamics simulations. Journal of Applied Physics, 134(24):244901, 12 2023. doi:10.1063/5.0176338.

  17. Niuchang Ouyang, Zezhu Zeng, Chen Wang, Qi Wang, and Yue Chen. Role of high-order lattice anharmonicity in the phonon thermal transport of silver halide $\mathrm Agx (x=\mathrm Cl,\mathrm Br,\mathrm I)$. Phys. Rev. B, 108:174302, Nov 2023. URL: https://link.aps.org/doi/10.1103/PhysRevB.108.174302, doi:10.1103/PhysRevB.108.174302.

  18. Shuning Pan, Tianheng Huang, Allona Vazan, Zhixin Liang, Cong Liu, Junjie Wang, Chris J. Pickard, Hui-Tian Wang, Dingyu Xing, and Jian Sun. Magnesium oxide-water compounds at megabar pressure and implications on planetary interiors. Nature Communications, 14(1):1165, 2023. doi:10.1038/s41467-023-36802-8.

  19. Petter Rosander, Erik Fransson, Cosme Milesi-Brault, Constance Toulouse, Frédéric Bourdarot, Andrea Piovano, Alexei Bossak, Mael Guennou, and Göran Wahnström. Anharmonicity of the antiferrodistortive soft mode in barium zirconate $\mathrm BaZrO_3$. Phys. Rev. B, 108:014309, 2023. doi:10.1103/PhysRevB.108.014309.

  20. Wenhao Sha, Xuan Dai, Siyu Chen, Binglun Yin, and Fenglin Guo. Phonon thermal transport in two-dimensional PbTe monolayers via extensive molecular dynamics simulations with a neuroevolution potential. Materials Today Physics, 34:101066, 2023. doi:10.1016/j.mtphys.2023.101066.

  21. Jiuyang Shi, Zhixing Liang, Junjie Wang, Shuning Pan, Chi Ding, Yong Wang, Hui-Tian Wang, Dingyu Xing, and Jian Sun. Double-shock compression pathways from diamond to bc8 carbon. Phys. Rev. Lett., 131:146101, 2023. doi:10.1103/PhysRevLett.131.146101.

  22. Yong-Bo Shi, Yuan-Yuan Chen, Hao Wang, Shuo Cao, Yuan-Xu Zhu, Meng-Fan Chu, Zhu-Feng Shao, Hai-Kuan Dong, and Ping Qian. Investigation of the mechanical and transport properties of InGeX₃ (X = S, Se and Te) monolayers using density functional theory and machine learning. Physical Chemistry Chemical Physics, 2023. doi:10.1039/D3CP01441J.

  23. Yongbo Shi, Yuanyuan Chen, Haikuan Dong, Hao Wang, and Ping Qian. Investigation of phase transition, mechanical behavior and lattice thermal conductivity of halogen perovskites using machine learning interatomic potentials. Phys. Chem. Chem. Phys., 25:30644–30655, 2023. doi:10.1039/D3CP04657E.

  24. Ye Su, Yuan-Yuan Chen, Hao Wang, Hai-Kuan Dong, Shuo Cao, Li-Bin Shi, and Ping Qian. Origin of low lattice thermal conductivity and mobility of lead-free halide double perovskites. Journal of Alloys and Compounds, 962:170988, 2023. doi:10.1016/j.jallcom.2023.170988.

  25. Qi Wang, Chen Wang, Cheng Chi, Niuchang Ouyang, Ruiqiang Guo, Nuo Yang, and Yue Chen. Phonon transport in freestanding $\text SrTiO_3$ down to the monolayer limit. Phys. Rev. B, 108:115435, 2023. doi:10.1103/PhysRevB.108.115435.

  26. Yanzhou Wang, Zheyong Fan, Ping Qian, Miguel A. Caro, and Tapio Ala-Nissila. Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations. Physical Review B, 107(5):054303, 2023. doi:10.1103/PhysRevB.107.054303.

  27. Han Wei, Yue Hu, and Hua Bao. Influence of point defects and multiscale pores on the different phonon transport regimes. Communications Materials, 4(1):1–9, 2023. doi:10.1038/s43246-023-00330-1.

  28. Julia Wiktor, Erik Fransson, Dominik Kubicki, and Paul Erhart. Quantifying dynamic tilting in halide perovskites: chemical trends and local correlations. Chemistry of Materials, 35:6737–6744, 2023. doi:10.1021/acs.chemmater.3c00933.

  29. Xin Wu, Xin Huang, Lei Yang, Zhongwei Zhang, Yangyu Guo, Sebastian Volz, Qiang Han, and Masahiro Nomura. Suppressed thermal transport in mathematically inspired 2d heterosystems. Carbon, 213:118264, 2023. doi:10.1016/j.carbon.2023.118264.

  30. Xin Wu, Penghua Ying, Chunlei Li, and Qiang Han. Dual effects of hetero-interfaces on phonon thermal transport across graphene/C₃N lateral superlattices. International Journal of Heat and Mass Transfer, 201:123643, 2023. doi:10.1016/j.ijheatmasstransfer.2022.123643.

  31. Jia-Hao Xiong, Zi-Jun Qi, Kang Liang, Xiang Sun, Zhan-Peng Sun, Qi-Jun Wang, Li-Wei Chen, Gai Wu, and Wei Shen. Molecular dynamics study of thermal conductivities of cubic diamond, lonsdaleite, and nanotwinned diamond via machine-learned potential. Chinese Physics B, 32(12):128101, dec 2023. URL: https://dx.doi.org/10.1088/1674-1056/ace4b4, doi:10.1088/1674-1056/ace4b4.

  32. Ke Xu, Yongchao Hao, Ting Liang, Penghua Ying, Jianbin Xu, Jianyang Wu, and Zheyong Fan. Accurate prediction of heat conductivity of water by a neuroevolution potential. The Journal of Chemical Physics, 05 2023. URL: https://doi.org/10.1063/5.0147039, doi:10.1063/5.0147039.

  33. Chao Yang, Jian Wang, Dezhi Ma, Zhiqiang Li, Zhiyuan He, Linhua Liu, Zhiwei Fu, and Jia-Yue Yang. Phonon transport across gan-diamond interface: the nontrivial role of pre-interface vacancy-phonon scattering. International Journal of Heat and Mass Transfer, 214:124433, 2023. doi:https://doi.org/10.1016/j.ijheatmasstransfer.2023.124433.

  34. Penghua Ying, Haikuan Dong, Ting Liang, Zheyong Fan, Zheng Zhong, and Jin Zhang. Atomistic insights into the mechanical anisotropy and fragility of monolayer fullerene networks using quantum mechanical calculations and machine-learning molecular dynamics simulations. Extreme Mechanics Letters, 58:101929, 2023. doi:10.1016/j.eml.2022.101929.

  35. Penghua Ying and Zheyong Fan. Combining the d3 dispersion correction with the neuroevolution machine-learned potential. Journal of Physics: Condensed Matter, 36(12):125901, dec 2023. doi:10.1088/1361-648X/ad1278.

  36. Penghua Ying, Ting Liang, Ke Xu, Jianbin Xu, Zheyong Fan, Tapio Ala-Nissila, and Zheng Zhong. Variable thermal transport in black, blue, and violet phosphorene from extensive atomistic simulations with a neuroevolution potential. International Journal of Heat and Mass Transfer, 202:123681, 2023. doi:10.1016/j.ijheatmasstransfer.2022.123681.

  37. Penghua Ying, Ting Liang, Ke Xu, Jin Zhang, Jianbin Xu, Zheng Zhong, and Zheyong Fan. Sub-micrometer phonon mean free paths in metal–organic frameworks revealed by machine learning molecular dynamics simulations. ACS Applied Materials & Interfaces, 15:36412, 2023. doi:10.1021/acsami.3c07770.

  38. Honggang Zhang, Xiaokun Gu, Zheyong Fan, and Hua Bao. Vibrational anharmonicity results in decreased thermal conductivity of amorphous $\mathrm HfO_2$ at high temperature. Phys. Rev. B, 108:045422, 2023. doi:10.1103/PhysRevB.108.045422.

  39. Rui Zhao, Shucheng Wang, Zhuangzhuang Kong, Yunlei Xu, Kuan Fu, Ping Peng, and Cuilan Wu. Development of a neuroevolution machine learning potential of pd-cu-ni-p alloys. Materials & Design, 231:112012, 2023. doi:10.1016/j.matdes.2023.112012.

  40. Ziyue Zhou, Jincheng Zeng, Zixuan Song, Yanwen Lin, Qiao Shi, Yongchao Hao, Yuequn Fu, Zhisen Zhang, and Jianyang Wu. Thermal conductivity of fivefold twinned silicon-germanium heteronanowires. Phys. Chem. Chem. Phys., 25:25368–25376, 2023. doi:10.1039/D3CP02926C.

2022

  1. Haikuan Dong, Zheyong Fan, Ping Qian, and Yanjing Su. Exactly equivalent thermal conductivity in finite systems from equilibrium and nonequilibrium molecular dynamics simulations. Physica E: Low-dimensional Systems and Nanostructures, 144:115410, 2022. doi:10.1016/j.physe.2022.115410.

  2. Zheyong Fan. Improving the accuracy of the neuroevolution machine learning potential for multi-component systems. Journal of Physics: Condensed Matter, 34(12):125902, 2022. doi:10.1088/1361-648X/ac462b.

  3. 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, and Tapio Ala-Nissila. GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations. The Journal of Chemical Physics, 157(11):114801, 2022. doi:10.1063/5.0106617.

  4. Hao Feng, Kai Zhang, Xin Wang, Guiqing Zhang, and Xiaoyong Guo. Thermal transport of bilayer graphene: a homogeneous nonequilibrium molecular dynamics study. Physica Scripta, 97(4):045704, 2022. doi:10.1088/1402-4896/ac5af0.

  5. Alexander J. Gabourie, Çağıl Köroğlu, and Eric Pop. Substrate-dependence of monolayer MoS₂ thermal conductivity and thermal boundary conductance. Journal of Applied Physics, 131(19):195103, 2022. doi:10.1063/5.0089247.

  6. Shuo Jin, Zhongwei Zhang, Yangyu Guo, Jie Chen, Masahiro Nomura, and Sebastian Volz. Optimization of interfacial thermal transport in Si/Ge heterostructure driven by machine learning. International Journal of Heat and Mass Transfer, 182:122014, 2022. doi:10.1016/j.ijheatmasstransfer.2021.122014.

  7. Keqiang Li, Yajuan Cheng, Maofeng Dou, Wang Zeng, Sebastian Volz, and Shiyun Xiong. Tuning the Anisotropic Thermal Transport in 110-Silicon Membranes with Surface Resonances. Nanomaterials, 12(1):123, 2022. doi:10.3390/nano12010123.

  8. Keqiang Li, Yajuan Cheng, Hongying Wang, Yangyu Guo, Zhongwei Zhang, Marc Bescond, Massahiro Nomura, Sebastian Volz, Xiaohong Zhang, and Shiyun Xiong. Phonon resonant effect in silicon membranes with different crystallographic orientations. International Journal of Heat and Mass Transfer, 183:122144, 2022. doi:10.1016/j.ijheatmasstransfer.2021.122144.

  9. T. Liang, K. Xu, M. Han, Y. Yao, Z. Zhang, X. Zeng, J. Xu, and J. Wu. Abnormally high thermal conductivity in fivefold twinned diamond nanowires. Materials Today Physics, 25:100705, 2022. doi:10.1016/j.mtphys.2022.100705.

  10. Wenhao Sha, Xuan Dai, Siyu Chen, and Fenglin Guo. Phonon thermal transport in graphene/h-BN superlattice monolayers. Diamond and Related Materials, 129:109341, 2022. doi:10.1016/j.diamond.2022.109341.

  11. Wenhao Sha and Fenglin Guo. Thermal transport in two-dimensional carbon nitrides: A comparative molecular dynamics study. Carbon Trends, 7:100161, 2022. doi:10.1016/j.cartre.2022.100161.

  12. Xiaomeng Wang, Yong Wang, Junjie Wang, Shuning Pan, Qing Lu, Hui-Tian Wang, Dingyu Xing, and Jian Sun. Pressure Stabilized Lithium-Aluminum Compounds with Both Superconducting and Superionic Behaviors. Physical Review Letters, 129(24):246403, 2022. doi:10.1103/PhysRevLett.129.246403.

  13. Xin Wu and Qiang Han. Maximum thermal conductivity of multilayer graphene with periodic two-dimensional empty space. International Journal of Heat and Mass Transfer, 191:122829, 2022. doi:10.1016/j.ijheatmasstransfer.2022.122829.

  14. Xin Wu and Qiang Han. Transition from incoherent to coherent phonon thermal transport across graphene/h-BN van der Waals superlattices. International Journal of Heat and Mass Transfer, 184:122390, 2022. doi:10.1016/j.ijheatmasstransfer.2021.122390.

  15. Xin Wu and Qiang Han. Tunable anisotropic in-plane thermal transport of multilayer graphene induced by 2D empty space: Insights from interfaces. Surfaces and Interfaces, 33:102296, 2022. doi:10.1016/j.surfin.2022.102296.

  16. Ke Xu, Ting Liang, Yuequn Fu, Zhen Wang, Zheyong Fan, Ning Wei, Jianbin Xu, Zhisen Zhang, and Jianyang Wu. Gradient nano-grained graphene as 2D thermal rectifier: A molecular dynamics based machine learning study. Applied Physics Letters, 121(13):133501, 2022. doi:10.1063/5.0108746.

  17. Penghua Ying, Ting Liang, Yao Du, Jin Zhang, Xiaoliang Zeng, and Zheng Zhong. Thermal transport in planar sp2-hybridized carbon allotropes: A comparative study of biphenylene network, pentaheptite and graphene. International Journal of Heat and Mass Transfer, 183:122060, 2022. doi:10.1016/j.ijheatmasstransfer.2021.122060.

  18. Ziyue Zhou, Ke Xu, Zixuan Song, Zhen Wang, Yanwen Lin, Qiao Shi, Yongchao Hao, Yuequn Fu, Zhisen Zhang, and Jianyang Wu. Isotope doping-induced crossover shift in the thermal conductivity of thin silicon nanowires. Journal of Physics: Condensed Matter, 35(8):085702, 2022. doi:10.1088/1361-648X/acab4a.

2021

  1. Giuseppe Barbalinardo, Zekun Chen, Haikuan Dong, Zheyong Fan, and Davide Donadio. Ultrahigh Convergent Thermal Conductivity of Carbon Nanotubes from Comprehensive Atomistic Modeling. Physical Review Letters, 127(2):025902, 2021. doi:10.1103/PhysRevLett.127.025902.

  2. Haikuan Dong, Shiyun Xiong, Zheyong Fan, Ping Qian, Yanjing Su, and Tapio Ala-Nissila. Interpretation of apparent thermal conductivity in finite systems from equilibrium molecular dynamics simulations. Physical Review B, 103(3):035417, 2021. doi:10.1103/PhysRevB.103.035417.

  3. Yao Du, Penghua Ying, and Jin Zhang. Prediction and optimization of the thermal transport in hybrid carbon-boron nitride honeycombs using machine learning. Carbon, 184:492–503, 2021. doi:10.1016/j.carbon.2021.08.035.

  4. Zheyong Fan, Zezhu Zeng, Cunzhi Zhang, Yanzhou Wang, Keke Song, Haikuan Dong, Yue Chen, and Tapio Ala-Nissila. Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport. Physical Review B, 104(10):104309, 2021. doi:10.1103/PhysRevB.104.104309.

  5. Shi En Kim, Fauzia Mujid, Akash Rai, Fredrik Eriksson, Joonki Suh, Preeti Poddar, Ariana Ray, Chibeom Park, Erik Fransson, Yu Zhong, David A. Muller, Paul Erhart, David G. Cahill, and Jiwoong Park. Extremely anisotropic van der Waals thermal conductors. Nature, 597(7878):660–665, 2021. doi:10.1038/s41586-021-03867-8.

  6. Nicholas W. Lundgren, Giuseppe Barbalinardo, and Davide Donadio. Mode localization and suppressed heat transport in amorphous alloys. Physical Review B, 103(2):024204, 2021. doi:10.1103/PhysRevB.103.024204.

  7. Hongying Wang, Yajuan Cheng, Zheyong Fan, Yangyu Guo, Zhongwei Zhang, Marc Bescond, Massahiro Nomura, Tapio Ala-Nissila, Sebastian Volz, and Shiyun Xiong. Anomalous thermal conductivity enhancement in low dimensional resonant nanostructures due to imperfections. Nanoscale, 13(22):10010–10015, 2021. doi:10.1039/D1NR01679B.

  8. Xin Wu and Qiang Han. Phonon Thermal Transport across Multilayer Graphene/Hexagonal Boron Nitride van der Waals Heterostructures. ACS Applied Materials & Interfaces, 13(27):32564–32578, 2021. doi:10.1021/acsami.1c08275.

  9. Xin Wu and Qiang Han. Semidefective Graphene/h-BN In-Plane Heterostructures: Enhancing Interface Thermal Conductance by Topological Defects. The Journal of Physical Chemistry C, 125(4):2748–2760, 2021. doi:10.1021/acs.jpcc.0c10387.

  10. Zhongwei Zhang, Yangyu Guo, Marc Bescond, Jie Chen, Masahiro Nomura, and Sebastian Volz. Generalized decay law for particlelike and wavelike thermal phonons. Physical Review B, 103(18):184307, 2021. doi:10.1103/PhysRevB.103.184307.

2020

  1. E. A. Bea, M. F. Carusela, A. Soba, A. G. Monastra, and A. M. Mancardo Viotti. Thermal conductance of structured silicon nanocrystals. Modelling and Simulation in Materials Science and Engineering, 28(7):075004, 2020. doi:10.1088/1361-651X/aba8eb.

  2. Haikuan Dong, Zheyong Fan, Ping Qian, Tapio Ala-Nissila, and Yanjing Su. Thermal conductivity reduction in carbon nanotube by fullerene encapsulation: A molecular dynamics study. Carbon, 161:800–808, 2020. doi:10.1016/j.carbon.2020.01.114.

  3. Bo Fu, Kevin D. Parrish, Hyun-Young Kim, Guihua Tang, and Alan J. H. McGaughey. Phonon confinement and transport in ultrathin films. Physical Review B, 101(4):045417, 2020. doi:10.1103/PhysRevB.101.045417.

  4. Alexander J. Gabourie, Saurabh V. Suryavanshi, Amir Barati Farimani, and Eric Pop. Reduced thermal conductivity of supported and encased monolayer and bilayer MoS₂. 2D Materials, 8(1):011001, 2020. doi:10.1088/2053-1583/aba4ed.

  5. Xin Wu and Qiang Han. Thermal conductivity of defective graphene: an efficient molecular dynamics study based on graphics processing units. Nanotechnology, 31(21):215708, 2020. doi:10.1088/1361-6528/ab73bc.

  6. Xin Wu and Qiang Han. Thermal conductivity of monolayer hexagonal boron nitride: From defective to amorphous. Computational Materials Science, 184:109938, 2020. doi:10.1016/j.commatsci.2020.109938.

2019

  1. Zheyong Fan, Haikuan Dong, Ari Harju, and Tapio Ala-Nissila. Homogeneous nonequilibrium molecular dynamics method for heat transport and spectral decomposition with many-body potentials. Phys. Rev. B, 99:064308, Feb 2019. doi:10.1103/PhysRevB.99.064308.

  2. Zheyong Fan, Yanzhou Wang, Xiaokun Gu, Ping Qian, Yanjing Su, and Tapio Ala-Nissila. A minimal Tersoff potential for diamond silicon with improved descriptions of elastic and phonon transport properties. Journal of Physics: Condensed Matter, 32(13):135901, 2019. doi:10.1088/1361-648X/ab5c5f.

  3. Xiaokun Gu, Zheyong Fan, Hua Bao, and C. Y. Zhao. Revisiting phonon-phonon scattering in single-layer graphene. Phys. Rev. B, 100:064306, Aug 2019. URL: https://link.aps.org/doi/10.1103/PhysRevB.100.064306, doi:10.1103/PhysRevB.100.064306.

  4. Leyla Isaeva, Giuseppe Barbalinardo, Davide Donadio, and Stefano Baroni. Modeling heat transport in crystals and glasses from a unified lattice-dynamical approach. Nature communications, 10(1):3853, 2019. doi:10.1038/s41467-019-11572-4.

  5. Zhen Li, Shiyun Xiong, Charles Sievers, Yue Hu, Zheyong Fan, Ning Wei, Hua Bao, Shunda Chen, Davide Donadio, and Tapio Ala-Nissila. Influence of thermostatting on nonequilibrium molecular dynamics simulations of heat conduction in solids. The Journal of Chemical Physics, 12 2019. URL: https://doi.org/10.1063/1.5132543, doi:10.1063/1.5132543.

  6. Ke Xu, Alexander J. Gabourie, Arsalan Hashemi, Zheyong Fan, Ning Wei, Amir Barati Farimani, Hannu-Pekka Komsa, Arkady V. Krasheninnikov, Eric Pop, and Tapio Ala-Nissila. Thermal transport in MoS₂ from molecular dynamics using different empirical potentials. Physical Review B, 99(5):054303, 2019. doi:10.1103/PhysRevB.99.054303.

2018

  1. Haikuan Dong, Zheyong Fan, Libin Shi, Ari Harju, and Tapio Ala-Nissila. Equivalence of the equilibrium and the nonequilibrium molecular dynamics methods for thermal conductivity calculations: From bulk to nanowire silicon. Physical Review B, 97(9):094305, 2018. doi:10.1103/PhysRevB.97.094305.

  2. Haikuan Dong, Petri Hirvonen, Zheyong Fan, and Tapio Ala-Nissila. Heat transport in pristine and polycrystalline single-layer hexagonal boron nitride. Physical Chemistry Chemical Physics, 20(38):24602–24612, 2018. doi:10.1039/C8CP05159C.

  3. Zheyong Fan, Ville Vierimaa, and Ari Harju. GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units. Computer Physics Communications, 230:113–120, 2018. doi:10.1016/j.cpc.2018.04.013.

  4. Petri Hirvonen, Gabriel Martine La Boissonière, Zheyong Fan, Cristian Vasile Achim, Nikolas Provatas, Ken R. Elder, and Tapio Ala-Nissila. Grain extraction and microstructural analysis method for two-dimensional poly and quasicrystalline solids. Physical Review Materials, 2(10):103603, 2018. doi:10.1103/PhysRevMaterials.2.103603.

  5. Bohayra Mortazavi, Meysam Makaremi, Masoud Shahrokhi, Zheyong Fan, and Timon Rabczuk. N-graphdiyne two-dimensional nanomaterials: Semiconductors with low thermal conductivity and high stretchability. Carbon, 137:57–67, 2018. doi:10.1016/j.carbon.2018.04.090.

  6. Ali Rajabpour, Zheyong Fan, and S. Mehdi Vaez Allaei. Inter-layer and intra-layer heat transfer in bilayer/monolayer graphene van der Waals heterostructure: Is there a Kapitza resistance analogous? Applied Physics Letters, 112(23):233104, 2018. doi:10.1063/1.5025604.

  7. Ke Xu, Zheyong Fan, Jicheng Zhang, Ning Wei, and Tapio Ala-Nissila. Thermal transport properties of single-layer black phosphorus from extensive molecular dynamics simulations. Modelling and Simulation in Materials Science and Engineering, 26(8):085001, 2018. doi:10.1088/1361-651X/aae180.

2017

  1. Khatereh Azizi, Petri Hirvonen, Zheyong Fan, Ari Harju, Ken R. Elder, Tapio Ala-Nissila, and S. Mehdi Vaez Allaei. Kapitza thermal resistance across individual grain boundaries in graphene. Carbon, 125:384–390, 2017. doi:10.1016/j.carbon.2017.09.059.

  2. Zheyong Fan, Wei Chen, Ville Vierimaa, and Ari Harju. Efficient molecular dynamics simulations with many-body potentials on graphics processing units. Computer Physics Communications, 218:10–16, 2017. doi:10.1016/j.cpc.2017.05.003.

  3. Zheyong Fan, Petri Hirvonen, Luiz Felipe C. Pereira, Mikko M. Ervasti, Ken R. Elder, Davide Donadio, Ari Harju, and Tapio Ala-Nissila. Bimodal Grain-Size Scaling of Thermal Transport in Polycrystalline Graphene from Large-Scale Molecular Dynamics Simulations. Nano Letters, 17(10):5919–5924, 2017. doi:10.1021/acs.nanolett.7b01742.

  4. Zheyong Fan, Luiz Felipe C. Pereira, Petri Hirvonen, Mikko M. Ervasti, Ken R. Elder, Davide Donadio, Tapio Ala-Nissila, and Ari Harju. Thermal conductivity decomposition in two-dimensional materials: application to graphene. Phys. Rev. B, 95:144309, Apr 2017. URL: https://link.aps.org/doi/10.1103/PhysRevB.95.144309, doi:10.1103/PhysRevB.95.144309.

  5. Zheyong Fan, Andreas Uppstu, and Ari Harju. Dominant source of disorder in graphene: charged impurities or ripples? 2D Materials, 4(2):025004, jan 2017. URL: https://dx.doi.org/10.1088/2053-1583/aa529b, doi:10.1088/2053-1583/aa529b.

  6. Petri Hirvonen, Zheyong Fan, Mikko M. Ervasti, Ari Harju, Ken R. Elder, and Tapio Ala-Nissila. Energetics and structure of grain boundary triple junctions in graphene. Scientific Reports, 7(1):4754, 2017. doi:10.1038/s41598-017-04852-w.

  7. Bohayra Mortazavi, Aurélien Lherbier, Zheyong Fan, Ari Harju, Timon Rabczuk, and Jean-Christophe Charlier. Thermal and electronic transport characteristics of highly stretchable graphene kirigami. Nanoscale, 9(42):16329–16341, 2017. doi:10.1039/C7NR05231F.

2016

  1. Petri Hirvonen, Mikko M. Ervasti, Zheyong Fan, Morteza Jalalvand, Matthew Seymour, S. Mehdi Vaez Allaei, Nikolas Provatas, Ari Harju, Ken R. Elder, and Tapio Ala-Nissila. Multiscale modeling of polycrystalline graphene: A comparison of structure and defect energies of realistic samples from phase field crystal models. Physical Review B, 94(3):035414, 2016. doi:10.1103/PhysRevB.94.035414.

  2. Bohayra Mortazavi, Zheyong Fan, Luiz Felipe C. Pereira, Ari Harju, and Timon Rabczuk. Amorphized graphene: A stiff material with low thermal conductivity. Carbon, 103:318–326, 2016. doi:10.1016/j.carbon.2016.03.007.

2015

  1. Zheyong Fan, Luiz Felipe C. Pereira, Hui-Qiong Wang, Jin-Cheng Zheng, Davide Donadio, and Ari Harju. Force and heat current formulas for many-body potentials in molecular dynamics simulations with applications to thermal conductivity calculations. Physical Review B, 92(9):094301, 2015. doi:10.1103/PhysRevB.92.094301.

2013

  1. Zheyong Fan, Topi Siro, and Ari Harju. Accelerated molecular dynamics force evaluation on graphics processing units for thermal conductivity calculations. Computer Physics Communications, 184(5):1414–1425, 2013. doi:10.1016/j.cpc.2013.01.008.