Enhancing thermal transport in multilayer structures: A molecular dynamics study on Lennard−Jones solids

Abstract

We investigate the thermal transport properties of three kinds of multilayer structures: a perfect superlattice (SL) structure, a quasi-periodic multilayer structure consisted of two superlattice (2SL) structures with different periods, and a random multilayer (RML) structure. Our simulation results show that there exists a large number of aperiodic multilayer structures that have effective thermal conductivity higher than that of the SL counterpart, showing enhancement ratio in the effective thermal conductivity up to 193%. Surprisingly, some RML structures also exhibit enhanced thermal transport than the SL counterpart even in the presence of phonon localization. The detailed analysis on the underlying mechanism reveals that such peculiar enhancement is caused by the synergistic effect of coherent and incoherent phonon transport, which can be tuned by the structural configuration. Combined with molecular dynamics simulations and the machine learning technique, we further reveal that the enhancement effect of the effective thermal conductivity by 2SL structure is more significant when the period of SL structure is close to the critical transition period between the coherent and incoherent phonon transport regimes. Our study proposes a novel strategy to enhance the thermal transport in multilayer structures by regulating the wave-particle duality of phonons via the structure optimization, which might provide valuable insights to the thermal management in devices with densely packed interfaces.

Graphical abstract

Keywords

multilayer structures / thermal conductivity / machine learning / molecular dynamics simulation / wave-particle duality of phonon

References

[1] Z.Zhang, Y.Ouyang, Y.Cheng, J.Chen, N.Li, G.Zhang. Size-dependent phononic thermal transport in low-dimensional nanomaterials. Phys. Rep. , 2020, 860 : 1

[2] W.Ren, Y.Ouyang, P.Jiang, C.Yu, J.He, J.Chen. The impact of interlayer rotation on thermal transport across graphene/hexagonal boron nitride van der Waals heterostructure. Nano Lett. , 2021, 21( 6): 2634

[3] C.Yu Y. Ouyang J.Chen, A perspective on the hydrodynamic phonon transport in two-dimensional materials, J. Appl. Phys. 130(1), 010902 ( 2021)

[4] G.Xie, D.Ding, G.Zhang. Phonon coherence and its effect on thermal conductivity of nanostructures. Adv. Phys. X , 2018, 3( 1): 1480417

[5] J.He, Y.Ouyang, C.Yu, P.Jiang, W.Ren, J.Chen. Lattice thermal conductivity of β₁₂ and χ₃ borophene. Chin. Phys. B , 2020, 29( 12): 126503

[6] A.L. Moore, L.Shi. Emerging challenges and materials for thermal management of electronics. Mater. Today , 2014, 17( 4): 163

[7] Y.Fu, J.Hansson, Y.Liu, S.Chen, A.Zehri, M.K. Samani, N.Wang, Y.Ni, Y.Zhang, Z.-B.Zhang. Graphene related materials for thermal management. 2D Mater. , 2019, 7 : 012001

[8] Y.Ouyang, Z.Zhang, D.Li, J.Chen, G.Zhang. Emerging theory, materials, and screening methods: New opportunities for promoting thermoelectric performance. Ann. Phys. , 2019, 531( 4): 1800437

[9] J.He, Y.Hu, D.Li, J.Chen. Ultra-low lattice thermal conductivity and promising thermoelectric figure of merit in borophene via chlorination. Nano Res. , 2022, 15( 4): 3804

[10] M.N. Luckyanova, J.Garg, K.Esfarjani, A.Jandl, M.T. Bulsara, A.J. Schmidt, A.J. Minnich, S.Chen, M.S. Dresselhaus, Z.Ren, E.A. Fitzgerald, G.Chen. Coherent phonon heat conduction in superlattices. Science , 2012, 338( 6109): 936

[11] J.Ravichandran, A.K. Yadav, R.Cheaito, P.B. Rossen, A.Soukiassian, S.Suresha, J.C. Duda, B.M. Foley, C.H. Lee, Y.Zhu, A.W. Lichtenberger, J.E. Moore, D.A. Muller, D.G. Schlom, P.E. Hopkins, A.Majumdar, R.Ramesh, M.A. Zurbuchen. Crossover from incoherent to coherent phonon scattering in epitaxial oxide superlattices. Nat. Mater. , 2014, 13( 2): 168

[12] T.Zhu, E.Ertekin. Phonon transport on two-dimensional graphene/boron nitride superlattices. Phys. Rev. B , 2014, 90( 19): 195209

[13] J.Maire, R.Anufriev, R.Yanagisawa, A.Ramiere, S.Volz, M.Nomura. Heat conduction tuning by wave nature of phonons. Sci. Adv. , 2017, 3( 8): e1700027

[14] P.Jiang, Y.Ouyang, W.Ren, C.Yu, J.He, J.Chen. Total-transmission and total-reflection of individual phonons in phononic crystal nanostructures. APL Mater. , 2021, 9( 4): 040703

[15] L.Yang, J.Chen, N.Yang, B.Li. Significant reduction of graphene thermal conductivity by phononic crystal structure. Int. J. Heat Mass Transf. , 2015, 91 : 428

[16] X.K. Chen, Z.X. Xie, W.X. Zhou, L.M. Tang, K.Q. Chen. Phonon wave interference in graphene and boron nitride superlattice. Appl. Phys. Lett. , 2016, 109( 2): 023101

[17] Z.Zhang, Y.Guo, M.Bescond, J.Chen, M.Nomura, S.Volz. Coherent thermal transport in nano-phononic crystals: An overview. APL Mater. , 2021, 9( 8): 081102

[18] Y.Zhou, X.Gong, B.Xu, M.Hu. First-principles and molecular dynamics study of thermoelectric transport properties of N-type silicon-based superlattice-nanocrystalline heterostructures. J. Appl. Phys. , 2017, 122( 8): 085105

[19] I.M. Felix, L.F. C. Pereira. Thermal conductivity of graphene−hBN superlattice ribbons. Sci. Rep. , 2018, 8( 1): 2737

[20] L.Razzaghi, F.Khoeini, A.Rajabpour, M.Khalkhali. Thermal transport in two-dimensional C₃N/C₂N superlattices: A molecular dynamics approach. Int. J. Heat Mass Transf. , 2021, 177 : 121561

[21] S.C. Huberman, J.M. Larkin, A.J. McGaughey, C.H. Amon. Disruption of superlattice phonons by interfacial mixing. Phys. Rev. B , 2013, 88( 15): 155311

[22] Y.Wang, C.Gu, X.Ruan. Optimization of the random multilayer structure to break the random-alloy limit of thermal conductivity. Appl. Phys. Lett. , 2015, 106( 7): 073104

[23] T.Juntunen, O.Vänskä, I.Tittonen. Anderson localization quenches thermal transport in aperiodic superlattices. Phys. Rev. Lett. , 2019, 122( 10): 105901

[24] E.Pop. Energy dissipation and transport in nanoscale devices. Nano Res. , 2010, 3( 3): 147

[25] C.Xiang, C.W. Wu, W.X. Zhou, G.Xie, G.Zhang. Thermal transport in lithium-ion battery: A micro perspective for thermal management. Front. Phys. , 2022, 17( 1): 13202

[26] S.Hu, Z.Zhang, P.Jiang, J.Chen, S.Volz, M.Nomura, B.Li. Randomness-induced phonon localization in graphene heat conduction. J. Phys. Chem. Lett. , 2018, 9( 14): 3959

[27] M.Luckyanova, J.Mendoza, H.Lu, B.Song, S.Huang, J.Zhou, M.Li, Y.Dong, H.Zhou, J.Garlow, L.Wu, B.J. Kirby, A.J. Grutter, A.A. Puretzky, Y.Zhu, M.S. Dresselhaus, A.Gossard, G.Chen. Phonon localization in heat conduction. Sci. Adv. , 2018, 4( 12): eaat9460

[28] S.Hu, Z.Zhang, P.Jiang, W.Ren, C.Yu, J.Shiomi, J.Chen. Disorder limits the coherent phonon transport in two-dimensional phononic crystal structures. Nanoscale , 2019, 11( 24): 11839

[29] T.Ma, C.-T.Lin, Y.Wang. The dimensionality effect on phonon localization in graphene/hexagonal boron nitride superlattices. 2D Mater. , 2020, 7 : 035029

[30] Y.Wang, H.Huang, X.Ruan. Decomposition of coherent and incoherent phonon conduction in superlattices and random multilayers. Phys. Rev. B , 2014, 90( 16): 165406

[31] H.Wei, H.Bao, X.Ruan. Genetic algorithm-driven discovery of unexpected thermal conductivity enhancement by disorder. Nano Energy , 2020, 71 : 104619

[32] P.Chakraborty, Y.Liu, T.Ma, X.Guo, L.Cao, R.Hu, Y.Wang. Quenching thermal transport in aperiodic superlattices: A molecular dynamics and machine learning study. ACS Appl. Mater. Interfaces , 2020, 12( 7): 8795

[33] A.Agrawal, A.Choudhary. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Mater. , 2016, 4( 5): 053208

[34] S.Ju, T.Shiga, L.Feng, Z.Hou, K.Tsuda, J.Shiomi. Designing nanostructures for phonon transport via Bayesian optimization. Phys. Rev. X , 2017, 7( 2): 021024

[35] X.Wan, W.Feng, Y.Wang, H.Wang, X.Zhang, C.Deng, N.Yang. Materials discovery and properties prediction in thermal transport via materials informatics: A mini review. Nano Lett. , 2019, 19( 6): 3387

[36] Y.Ouyang, Z.Zhang, C.Yu, J.He, G.Yan, J.Chen. Accuracy of machine learning potential for predictions of multiple-target physical properties. Chin. Phys. Lett. , 2020, 37( 12): 126301

[37] S.Ju, S.Shimizu, J.Shiomi. Designing thermal functional materials by coupling thermal transport calculations and machine learning. J. Appl. Phys. , 2020, 128( 16): 161102

[38] P.R. Chowdhury, C.Reynolds, A.Garrett, T.Feng, S.P. Adiga, X.Ruan. Machine learning maximized Anderson localization of phonons in aperiodic superlattices. Nano Energy , 2020, 69 : 104428

[39] Y.Ouyang, C.Yu, G.Yan, J.Chen. Machine learning approach for the prediction and optimization of thermal transport properties. Front. Phys. , 2021, 16( 4): 43200

[40] L.Yang, X.Wan, D.Ma, Y.Jiang, N.Yang. Maximization and minimization of interfacial thermal conductance by modulating the mass distribution of the interlayer. Phys. Rev. B , 2021, 103( 15): 155305

[41] S.Arabha, Z.S. Aghbolagh, K.Ghorbani, S.M. Hatam-Lee, A.Rajabpour. Recent advances in lattice thermal conductivity calculation using machine-learning interatomic potentials. J. Appl. Phys. , 2021, 130( 21): 210903

[42] Y.Ouyang, C.Yu, J.He, P.Jiang, W.Ren, J.Chen. Accurate description of high-order phonon anharmonicity and lattice thermal conductivity from molecular dynamics simulations with machine learning potential. Phys. Rev. B , 2022, 105( 11): 115202

[43] Z.Zhang, Y.Guo, M.Bescond, J.Chen, M.Nomura, S.Volz. Generalized decay law for particlelike and wavelike thermal phonons. Phys. Rev. B , 2021, 103( 18): 184307

[44] Z.Zhang, Y.Guo, M.Bescond, J.Chen, M.Nomura, S.Volz. Heat conduction theory including phonon coherence. Phys. Rev. Lett. , 2022, 128( 1): 015901

[45] S.Plimpton. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. , 1995, 117( 1): 1

[46] P.Chakraborty, I.A. Chiu, T.Ma, Y.Wang. Complex temperature dependence of coherent and incoherent lattice thermal transport in superlattices. Nanotechnology , 2021, 32( 6): 065401

[47] A.Giri, J.L. Braun, P.E. Hopkins. Implications of interfacial bond strength on the spectral contributions to thermal boundary conductance across solid, liquid, and gas interfaces: A molecular dynamics study. J. Phys. Chem. C , 2016, 120( 43): 24847

[48] P.Chakraborty, L.Cao, Y.Wang. Ultralow lattice thermal conductivity of the random multilayer structure with lattice imperfections. Sci. Rep. , 2017, 7( 1): 8134

[49] B.Qiu, G.Chen, Z.Tian. Effects of aperiodicity and roughness on coherent heat conduction in superlattices. Nanoscale Microscale Thermophys. Eng. , 2015, 19( 4): 272

[50] Y.Zhou, X.Zhang, M.Hu. An excellent candidate for largely reducing interfacial thermal resistance: A nano-confined mass graded interface. Nanoscale , 2016, 8( 4): 1994

[51] K.Sääskilahti, J.Oksanen, S.Volz, J.Tulkki. Frequency-dependent phonon mean free path in carbon nanotubes from nonequilibrium molecular dynamics. Phys. Rev. B , 2015, 91( 11): 115426

[52] K.Sääskilahti, J.Oksanen, J.Tulkki, S.Volz. Role of anharmonic phonon scattering in the spectrally decomposed thermal conductance at planar interfaces. Phys. Rev. B , 2014, 90( 13): 134312

[53] Y.Zhou, X.Zhang, M.Hu. Quantitatively analyzing phonon spectral contribution of thermal conductivity based on nonequilibrium molecular dynamics simulations (I): From space Fourier transform. Phys. Rev. B , 2015, 92( 19): 195204

[54] Y.Zhou, M.Hu. Full quantification of frequency-dependent interfacial thermal conductance contributed by two- and three-phonon scattering processes from nonequilibrium molecular dynamics simulations. Phys. Rev. B , 2017, 95( 11): 115313

[55] Y.Ouyang, Z.Zhang, Q.Xi, P.Jiang, W.Ren, N.Li, J.Zhou, J.Chen. Effect of boundary chain folding on thermal conductivity of lamellar amorphous polyethylene. RSC Advances , 2019, 9( 57): 33549

[56] X.K. Chen, M.Pang, T.Chen, D.Du, K.Q. Chen. Thermal rectification in asymmetric graphene/hexagonal boron nitride van der Waals heterostructures. ACS Appl. Mater. Interfaces , 2020, 12( 13): 15517

[57] Y.Ma, Z.Zhang, J.Chen, K.Sääskilahti, S.Volz, J.Chen. Ordered water layers by interfacial charge decoration leading to an ultra-low Kapitza resistance between graphene and water. Carbon , 2018, 135 : 263

[58] G.Chen, Nanoscale Energy Transport and Conversion: A Parallel Treatment of Electrons, Molecules, Phonons, and Photons, Oxford University Press, 2005

[59] E.S. Landry, A.J. McGaughey. Effect of film thickness on the thermal resistance of confined semiconductor thin films. J. Appl. Phys. , 2010, 107( 1): 013521

[60] S.I. Tamura, Y.Tanaka, H.J. Maris. Phonon group velocity and thermal conduction in superlattices. Phys. Rev. B , 1999, 60( 4): 2627

[61] R.Hu, S.Iwamoto, L.Feng, S.Ju, S.Hu, M.Ohnishi, N.Nagai, K.Hirakawa, J.Shiomi. Machine-learning-optimized aperiodic superlattice minimizes coherent phonon heat conduction. Phys. Rev. X , 2020, 10( 2): 021050

[62] P.Jiang, S.Hu, Y.Ouyang, W.Ren, C.Yu, Z.Zhang, J.Chen. Remarkable thermal rectification in pristine and symmetric monolayer graphene enabled by asymmetric thermal contact. J. Appl. Phys. , 2020, 127( 23): 235101

[63] C.Yu, Y.Hu, J.He, S.Lu, D.Li, J.Chen. Strong four-phonon scattering in monolayer and hydrogenated bilayer BAs with horizontal mirror symmetry. Appl. Phys. Lett. , 2022, 120( 13): 132201

[64] S.Lu, W.Ren, J.He, C.Yu, P.Jiang, J.Chen. Enhancement of the lattice thermal conductivity of two-dimensional functionalized MXenes by inversion symmetry breaking. Phys. Rev. B , 2022, 105( 16): 165301

[65] T.Zhan, L.Fang, Y.Xu. Prediction of thermal boundary resistance by the machine learning method. Sci. Rep. , 2017, 7( 1): 7109

[66] Y.J. Wu, M.Sasaki, M.Goto, L.Fang, Y.Xu. Electrically conductive thermally insulating Bi–Si nanocomposites by interface design for thermal management. ACS Appl. Nano Mater. , 2018, 1( 7): 3355

[67] Y.J. Wu, L.Fang, Y.Xu. Predicting interfacial thermal resistance by machine learning. npj Comput. Mater. , 2019, 5 : 56

[68] Y.Liu, W.Hong, B.Cao. Machine learning for predicting thermodynamic properties of pure fluids and their mixtures. Energy , 2019, 188 : 116091

[69] Z.Hou, Y.Takagiwa, Y.Shinohara, Y.Xu, K.Tsuda. Machine-learning-assisted development and theoretical consideration for the Al₂Fe₃Si₃ thermoelectric material. ACS Appl. Mater. Interfaces , 2019, 11( 12): 11545

[70] J.H. Friedman. Greedy function approximation: A gradient boosting machine. Ann. Stat. , 2001, 29( 5): 1189

[71] M.R. Wagner, B.Graczykowski, J.S. Reparaz, A.El Sachat, M.Sledzinska, F.Alzina, C.M. Sotomayor Torres. Two-dimensional phononic crystals: Disorder matters. Nano Lett. , 2016, 16( 9): 5661

Declaration of interests

The authors declare no competing financial interest.

Electronic supplementary material

is available in the online version of this article at https://doi.org/10.1007/s11467-022-1170-5 and https://journal.hep.com.cn/fop/EN/10.1007/s11467-022-1170-5 and are accessible for authorized users.

Acknowledgements

This project was supported in part by the grants from the National Natural Science Foundation of China (Grant Nos. 12075168 and 11890703), the Science and Technology Commission of Shanghai Municipality (Grant Nos. 19ZR1478600 and 21JC1405600), and the Fundamental Research Funds for the Central Universities (Grant No. 22120220060).

RIGHTS & PERMISSIONS

2022 Higher Education Press