[1] [1] HUANG X, GUO Z. Thermal effect of epilayer on phonon transport of semiconducting heterostructure interfaces[J]. Int J Heat Mass Transfer, 2021, 178: 121613.

[2] [2] WU J, ZHOU E, QIN Z, et al. Accessing negative Poisson’s ratio of graphene by machine learning interatomic potentials[J]. Nanotechnology, 2022, 33(27): 275710.

[3] [3] DU Y, MAASSEN J, WU W, et al. Auxetic black phosphorus: A 2D material with negative Poisson’s ratio[J]. Nano Lett, 2016, 16(10): 6701-6708.

[4] [4] FAN Y, XIANG Y, SHEN H S. Temperature-dependent negative Poisson’s ratio of monolayer graphene: Prediction from molecular dynamics simulations[J]. Nanotechnol Rev, 2019, 8(1): 415-421.

[5] [5] GRIMA J N, WINCZEWSKI S, MIZZI L, et al. Tailoring graphene to achieve negative Poisson’s ratio properties[J]. Adv Mater, 2015, 27(8): 1455-1459.

[6] [6] HO D T, HO V H, PARK H S, et al. Negative in-plane Poisson’s ratio for single layer black phosphorus: An atomistic simulation study[J]. Phys Status Solidi (b), 2017, 254(12): 1700285.

[7] [7] CAI Y, KANG S, XU X. Extremely high tensile strength and superior thermal conductivity of an sp3-hybridized superhard C24 fullerene crystal[J]. J Mater Chem A, 2019, 7(7): 3426-3431.

[8] [8] CAI Q, SCULLION D, GAN W, et al. High thermal conductivity of high-quality monolayer boron nitride and its thermal expansion[J]. Sci Adv, 2019, 5(6): eaav0129.

[9] [9] CARRETE J, LI W, MINGO N, et al. Finding unprecedentedly low-thermal-conductivity half-heusler semiconductors via high-throughput materials modeling[J]. Phys Rev X, 2014, 4(1): 011019.

[10] [10] CUI Y, QIN Z, WU H, et al. Flexible thermal interface based on self-assembled boron arsenide for high-performance thermal management[J]. Nat Commun, 2021, 12(1): 1284.

[11] [11] ZHENG Q, LI S, LI C, et al. High thermal conductivity in isotopically enriched cubic boron phosphide[J]. Adv Funct Mater, 2018, 28(43): 1805116.

[12] [12] XIE X, YANG K, LI D, et al. High and low thermal conductivity of amorphous macromolecules[J]. Phys Rev B, 2017, 95(3): 035406.

[13] [13] FENG T, RUAN X. Ultra-low thermal conductivity in graphene nanomesh[J]. Carbon, 2016, 101: 107-113.

[14] [14] SIVARAMAN G, KRISHNAMOORTHY A N, BAUR M, et al. Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide[J]. npj Comput Mater, 2020, 6(1): 104.

[15] [15] ROWE P, DERINGER V L, GASPAROTTO P, et al. An accurate and transferable machine learning potential for carbon[J]. J Chem Phys, 2020, 153(3): 034702.

[16] [16] ROWE P, CS?NYI G, ALF? D, et al. Development of a machine learning potential for graphene[J]. Phys Rev B, 2018, 97(5): 054303.

[17] [17] QIAN X, PENG S, LI X, et al. Thermal conductivity modeling using machine learning potentials: Application to crystalline and amorphous silicon[J]. Mater Today Phys, 2019, 10: 100140.

[18] [18] PODRYABINKIN E V, TIKHONOV E V, SHAPEEV A V, et al. Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning[J]. Phys Rev B, 2019, 99(6): 064114.

[19] [19] ROWE P, DERINGER V L, GASPAROTTO P, et al. An accurate and transferable machine learning potential for carbon[J]. J Chem Phys, 2020, 153(3): 034702.

[20] [20] PERILLA J R, GOH B C, CASSIDY C K, et al. Molecular dynamics simulations of large macromolecular complexes[J]. Curr Opinion Struct Biol, 2015, 31: 64-74.

[21] [21] ZHANG X, XIE H, HU M, et al. Thermal conductivity of silicene calculated using an optimized Stillinger-Weber potential[J]. Phys Rev B, 2014, 89(5): 054310.

[22] [22] YUE S Y, QIN G, ZHANG X, et al. Thermal transport in novel carbon allotropes with sp2 or sp3 hybridization: An ab initio study[J]. Phy Rev B, 2017, 95(8): 085207.

[23] [23] QIN G, HAO K R, YAN Q B, et al. Exploring T-carbon for energy applications[J]. Nanoscale, 2019, 11(13): 5798-5806.

[24] [24] QIN G, QIN Z, FANG W Z, et al. Diverse anisotropy of phonon transport in two-dimensional group IV-VI compounds: A comparative study[J]. Nanoscale, 2016, 8(21): 11306-11319.

[25] [25] QIN G, QIN Z, WANG H, et al. Anomalously temperature-dependent thermal conductivity of monolayer GaN with large deviations from the traditional 1/T law[J]. Phys Rev B, 2017, 95(19): 195416.

[26] [26] HANAKATA P Z, CUBUK E D, CAMPBELL D K, et al. Accelerated search and design of stretchable graphene kirigami using machine learning[J]. Phys Rev Lett, 2018, 121(25): 255304.

[27] [27] CHAN H, NARAYANAN B, CHERUKARA M J, et al. Machine learning classical interatomic potentials for molecular dynamics from first-principles training data[J]. J Phys Chem C, 2019, 123(12): 6941-6957.

[28] [28] CHEN L, TRAN H, BATRA R, et al. Machine learning models for the lattice thermal conductivity prediction of inorganic materials[J]. Comput Mater Sci, 2019, 170: 109155.

[29] [29] KOROTAEV P. Accessing thermal conductivity of complex compounds by machine learning interatomic potentials[J]. Phys Rev B, 2019, 100: 144308.

[30] [30] LIU H, QIAN X, BAO H, et al. High-temperature phonon transport properties of SnSe from machine-learning interatomic potential[J]. J Phys: Condensed Matter, 2021, 33(40): 405401.

[31] [31] MORTAZAVI B, NOVIKOV I S, SHAPEEV A V. A machine-learning-based investigation on the mechanical/failure response and thermal conductivity of semiconducting BC2N monolayers[J]. Carbon, 2022, 188: 431-441.

[32] [32] MORTAZAVI B, PODRYABINKIN E V, NOVIKOV I S, et al. Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials[J]. J Phys: Materials, 2020, 3(2): 02LT02.

[33] [33] MORTAZAVI B, JAVVAJI B, SHOJAEI F, et al. Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles[J]. Nano Energy, 2021, 82: 105716.

[34] [34] BLANK T B, BROWN S D, CALHOUN A W, et al. Neural network models of potential energy surfaces[J]. J Chem Phys, 1995, 103(10): 4129-4137.

[35] [35] BEHLER J, PARRINELLO M. Generalized neural-network representation of high-dimensional potential-energy surfaces[J]. Phys Rev Lett, 2007, 98(14): 146401.

[36] [36] SHAPEEV A V. Moment tensor potentials: A class of systematically improvable interatomic potentials[J]. Multiscale Model Simul, 2016, 14(3): 1153-1173.

[37] [37] BART?K A P, PAYNE M C, KONDOR R, et al. Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons[J]. Phys Rev Lett, 2010, 104(13): 136403.

[38] [38] FAN Z, ZENG Z, ZHANG C, et al. Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport[J]. Phys Rev B, 2021, 104(10): 104309.

[39] [39] WANG H, ZHANG L, HAN J, et al. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics[J]. Comput Phys Commun, 2018, 228: 178-184.

[40] [40] CALEGARI ANDRADE M F, KO H Y, ZHANG L, et al. Free energy of proton transfer at the water-TiO2 interface from ab initio deep potential molecular dynamics[J]. Chem Sci, 2020, 11(9): 2335-2341.

[41] [41] DAI F Z, SUN Y, WEN B, et al. Temperature dependent thermal and elastic properties of high entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: molecular dynamics simulation by deep learning potential[J]. J Mater Sci Technol, 2021, 72: 8-15.

[42] [42] GARTNER T E, ZHANG L, PIAGGI P M, et al. Signatures of a liquid-liquid transition in an ab initio deep neural network model for water[J]. Proceed National Academy Sci, 2020, 117(42): 26040-26046.

[43] [43] LI R, LEE E, LUO T. A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases[J]. Mater Today Phys, 2020, 12: 100181.

[44] [44] SHI M, MO P, LIU J. Deep neural network for accurate and efficient atomistic modeling of phase change memory[J]. IEEE Electron Device Lett, 2020, 41(3): 365-368.

[45] [45] ZHANG Y, WANG H, CHEN W, et al. DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models[J]. Comput Phys Commun, 2020, 253: 107206.

[46] [46] MORTAZAVI B, PODRYABINKIN E V, NOVIKOV I S, et al. Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution[J]. Comput Phys Commun, 2021, 258: 107583.

[47] [47] NOVIKOV I S, GUBAEV K, PODRYABINKIN E, et al. The MLIP package: Moment tensor potentials with mpi and active learning[J]. Machine Learn: Sci Technol, 2020, 2: 025002 .

[48] [48] NOVOSELOV I I, YANILKIN A V, SHAPEEV A V, et al. Moment tensor potentials as a promising tool to study diffusion processes[J]. Comput Mater Sci, 2019, 164: 46-56.

[49] [49] SUN P P, BAI L, KRIPALANI D R, et al. A new carbon phase with direct bandgap and high carrier mobility as electron transport material for perovskite solar cells[J]. npj Comput Mater, 2019, 5(1): 9.

[50] [50] FAN Z, WANG Y, YING P, et al. GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations[J]. J Chem Phys, 2022, 157(11): 114801.

[51] [51] DERINGER V L, CS?NYI G. Machine learning based interatomic potential for amorphous carbon[J]. Phys Rev B, 2017, 95(9): 094203.

[52] [52] BONATI L, PARRINELLO M. Silicon liquid structure and crystal nucleation from Ab Initio deep metadynamics[J]. Phys Rev Lett, 2018, 121(26): 265701.

[53] [53] YANG M, BONATI L, POLINO D, et al. Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water[J]. Catal Today, 2022, 387: 143?149.

[54] [54] ZHU Q, ZENG Q, OGANOV A R. Systematic search for low-enthalpy sp3 carbon allotropes using evolutionary metadynamics[J]. Physical Review B, 2012, 85(20): 201407.

[55] [55] ZENG J, CAO L, XU M, et al. Complex reaction processes in combustion unraveled by neural network based molecular dynamics simulation[J]. Nat Commun 2020, 11: 5713.

[56] [56] CHEN J, GU X, CAO B. A review of simulation methods in micro/nanoscale heat conduction[J]. ES Energy Environ, 2018, 1(39): 16-55.

[57] [57] LI W, CARRETE J, A. KATCHO N, et al. ShengBTE: A solver of the Boltzmann transport equation for phonons[J]. Comput Phys Commun, 2014, 185(6): 1747-1758.

[58] [58] HANSSON T, OOSTENBRINK C, VAN GUNSTEREN W. Molecular dynamics simulations[J]. Current Opinion Struct Biol, 2002, 12(2): 190-196.

[59] [59] MINGO N, YANG L. Phonon transport in nanowires coated with an amorphous material: An atomistic Green’s function approach[J]. Phys Rev B, 2003, 68(24): 245406.

[60] [60] OZPINECI A, CIRACI S. Quantum effects of thermal conductance through atomic chains[J]. Phys Rev B, 2001, 63(12): 125415.

[61] [61] SEGAL D, NITZAN A, H?NGGI P. Thermal conductance through molecular wires[J]. J Chem Phys, 2003, 119(13): 6840-6855.

[62] [62] JENG M S, YANG R, SONG D, et al. Modeling the thermal conductivity and phonon transport in nanoparticle composites using monte carlo simulation[J]. J Heat Transfer, 2008, 130(4): 042410.

[63] [63] FUCHS F, FURTHM?LLER J, BECHSTEDT F, et al. Quasiparticle band structure based on a generalized Kohn-Sham scheme[J]. Phys Rev B, 2007, 76(11): 115109.

[64] [64] GIANNOZZI P, ANDREUSSI O, BRUMME T, et al. Advanced capabilities for materials modelling with Quantum ESPRESSO[J]. J Phys: Condensed Matter, 2017, 29(46): 465901.

[65] [65] STEVEPLIMPTON. Fast parallel algorithms for short-range molecular dynamics[J]. J Computa Phys, 1995, 117(1): 1-19.

[66] [66] DONG H, HIRVONEN P, FAN Z, et al. Heat transport across graphene/hexagonal-BN tilted grain boundaries from phase-field crystal model and molecular dynamics simulations[J]. J Appl Phys, 2021, 130(23): 235102.

[67] [67] FAN Z, CHEN W, VIERIMAA V, et al. Efficient molecular dynamics simulations with many-body potentials on graphics processing units[J]. Comput Phys Commun, 2017, 218: 10-16.

[68] [68] FAN Z, ZENG Z, ZHANG C, et al. Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport[J]. Phys Rev B, 2021, 104(10): 104309.

[69] [69] GABOURIE A J, FAN Z, ALA-NISSILA T, et al. Spectral decomposition of thermal conductivity: Comparing velocity decomposition methods in homogeneous molecular dynamics simulations[J]. Phys Rev B, 2021, 103(20): 205421.

[70] [70] JENG M S, YANG R, SONG D, et al. Modeling the thermal conductivity and phonon transport in nanoparticle composites using monte carlo simulation[J]. J Heat Transfer, 2008, 130(4): 042410.

[71] [71] FLOURY J, CARSON J, PHAM Q T. Modelling thermal conductivity in heterogeneous media with the finite element method[J]. Food Bioproc Technol, 2008, 1(2): 161-170.

[72] [72] LENGVARSK? P, BOCKO J. Prediction of Young’s modulus of graphene sheets by the finite element method[J]. Am J Mech Eng, 2015, 3(6): 225-229.

[73] [73] MINGO N, YANG L. Phonon transport in nanowires coated with an amorphous material: An atomistic Green’s function approach[J]. Phys Rev B, 2003, 68(24): 245406.

[74] [74] BRANDBYGE M, MOZOS J L, ORDEJ?N P, et al. Density-functional method for nonequilibrium electron transport[J]. Phys Rev B, 2002, 65(16): 165401.

[75] [75] DONG J, ZHANG B, ZHANG S, et al. Effects of interface charge-transfer doping on thermoelectric transport properties of black phosphorene-F4TCNQ nanoscale devices[J]. Appl Surface Sci, 2022, 579: 152155.

[76] [76] TAO Y, CAI S, WU C, et al. Anisotropic phonon transport in van der Waals nanostructures[J]. Phys Lett A, 2022, 427: 127920.

[77] [77] GU X, FAN Z, BAO H. Thermal conductivity prediction by atomistic simulation methods: Recent advances and detailed comparison[J]. J Appl Phys, 2021, 130(21): 210902.

[78] [78] JIANG P, QIAN X, YANG R. Tutorial: Time-domain thermoreflectance (TDTR) for thermal property characterization of bulk and thin film materials[J]. J Appl Phys, 2018, 124(16): 161103.

[79] [79] ZHANG Y, ESLAMISARAY M A, FENG T, et al. Observation of suppressed diffuson and propagon thermal conductivity of hydrogenated amorphous silicon films[J]. Nanoscale Adv, 2022, 4(1): 87-94.

[80] [80] WANG M, RAMER G, PEREZ-MORELO D J, et al. High throughput nanoimaging of thermal conductivity and interfacial thermal conductance[J]. Nano Lett, 2022, 22(11): 4325-4332.

[81] [81] THAKUR S, DIONNE C J, KARNA P, et al. Density and atomic coordination dictate vibrational characteristics and thermal conductivity of amorphous silicon carbide[J]. Phys Rev Mater, 2022, 6(9): 094601.

[82] [82] JIANG P, WANG D, XIANG Z, et al. A new spatial-domain thermoreflectance method to measure a broad range of anisotropic in-plane thermal conductivity[J]. Int J Heat Mass Transfer, 2022, 191: 122849.

[83] [83] ALAGHEMANDI M, ALGAER E, B?HM M C, et al. The thermal conductivity and thermal rectification of carbon nanotubes studied using reverse non-equilibrium molecular dynamics simulations[J]. Nanotechnology, 2009, 20(11): 115704.

[84] [84] CHEN W, ZHANG J, YUE Y. Molecular dynamics study on thermal transport at carbon nanotube interface junctions: Effects of mechanical force and chemical functionalization[J]. Int J Heat Mass Transfer, 2016, 103: 1058-1064.

[85] [85] BART?K A P, KERMODE J, BERNSTEIN N, et al. Machine learning a general-purpose interatomic potential for silicon[J]. Phys Rev X, 2018, 8(4): 041048.

[86] [86] BUTLER K T, DAVIES D W, CARTWRIGHT H, et al. Machine learning for molecular and materials science[J]. Nature, 2018, 559(7715): 547-555.

[87] [87] CHAN H, SASIKUMAR K, SRINIVASAN S, et al. Machine learning a bond order potential model to study thermal transport in WSe2 nanostructures[J]. Nanoscale, 2019, 11(21): 10381-10392.

[88] [88] CHEN L, TRAN H, BATRA R, et al. Machine learning models for the lattice thermal conductivity prediction of inorganic materials[J]. Comput Mater Sci, 2019, 170: 109155.

[89] [89] KOROTAEV P. Accessing thermal conductivity of complex compounds by machine learning interatomic potentials[J]. Phys Rev B, 2019, 100(14): 144308.

[90] [90] STUART S J, TUTEIN A B, HARRISON J A. A reactive potential for hydrocarbons with intermolecular interactions[J]. J Chem Phys, 2000, 112(14): 6472-6486.

[91] [91] BRENNER D W, SHENDEROVA O A, HARRISON J A, et al. A second-generation reactive empirical bond order (REBO) potential energy expression for hydrocarbons[J]. J Phys: Condensed Matter, 2002, 14(4): 783.

[92] [92] SEVIK C, KINACI A, HASKINS J B. Influence of disorder on thermal transport properties of boron nitride nanostructures[J]. Phys Rev B, 2012, 86(7): 075403.

[93] [93] JIANG J W, CHANG T, GUO X, et al. Intrinsic negative Poisson’s ratio for single-layer graphene[J]. Nano Lett, 2016, 16(8): 5286-5290.

[94] [94] QIN Z, QIN G, HU M. Origin of anisotropic negative Poisson’s ratio in graphene[J]. Nanoscale, 2018, 10(22): 10365-10370.

[95] [95] DAI F Z, WEN B, SUN Y, et al. Theoretical prediction on thermal and mechanical properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by deep learning potential[J]. J Mater Sci Technol, 2020, 43: 168-174.

[96] [96] HEO S H, YOO J, LEE H, et al. Solution-processed hole-doped snse thermoelectric thin-film devices for low-temperature power generation[J]. ACS Energy Lett, 2022, 7(6): 2092-2101.

[97] [97] OUYANG N, WANG C, CHEN Y. Temperature- and pressure-dependent phonon transport properties of SnS across phase transition from machine-learning interatomic potential[J]. Int J Heat Mass Transfer, 2022, 192: 122859.

[98] [98] GUO D, LI C, LI K, et al. The thermoelectric performance of new structure SnSe studied by quotient graph and deep learning potential[J]. Mater Today Energy, 2021, 20: 100665.

[99] [99] MORTAZAVI B, SILANI M, PODRYABINKIN E V, et al. First-principles multiscale modeling of mechanical properties in graphene/borophene heterostructures empowered by machine-learning interatomic potentials[J]. Adv Mater, 2021: 2102807.

[100] [100] MORTAZAVI B. Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures[J]. Mater Horizons, 2020, 9: 2359-2367.

[101] [101] HE R, WU H, ZHANG L, et al. Structural phase transitions in SrTiO3 from deep potential molecular dynamics[J]. Phys Rev B, 2022, 105(6): 064104.

[102] [102] KIM S E, MUJID F, RAI A, et al. Extremely anisotropic van der Waals thermal conductors[J]. Nature, 2021, 597(7878): 660-665.

[103] [103] LI R, LEE E, LUO T. A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases[J]. Mater Today Phys, 2020, 12: 100181.

[104] [104] PEGOLO P, BARONI S, GRASSELLI F. Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations[J]. npj Comput Mater, 2022, 8(1): 24.

[105] [105] TIAN F, SONG B, CHEN X, et al. Unusual high thermal conductivity in boron arsenide bulk crystals[J]. Science, 2018, 361(6402): 582-585.

[106] [106] DAMES C. Ultrahigh thermal conductivity confirmed in boron arsenide[J]. Science, 2018, 361(6402): 549-550.

[107] [107] KANG J S, LI M, WU H, et al. Experimental observation of high thermal conductivity in boron arsenide[J]. Science, 2018, 361(6402): 575-578.

[108] [108] LI S, ZHENG Q, LV Y, et al. High thermal conductivity in cubic boron arsenide crystals[J]. Science, 2018, 361(6402): 579-581.

[109] [109] KANG J S, LI M, WU H, et al. Integration of boron arsenide cooling substrates into gallium nitride devices[J]. Nat Electron, 2021, 4(6): 416-423.

[110] [110] WU J, ZHOU E, HUANG A, et al. Deep-potential driven multiscale simulation of gallium nitride devices on boron arsenide cooling substrates[J]. arXiv Preprint, 2022: 2201.00516.

[111] [111] LIU Y, GUO B, ZOU X, et al. Machine learning assisted materials design and discovery for rechargeable batteries[J]. Energy Storage Mater, 2020, 31: 434-450.

[112] [112] UNKE O T, CHMIELA S, GASTEGGER M, et al. SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects[J]. Nat Commun, 2021, 12(1): 7273.