journal article·2024-12-03Open access

Dynamic mesophase transition induces anomalous suppressed and anisotropic phonon thermal transport

Linfeng Yu, Kexin Dong, Qi Yang, Yi Zhang, Zheyong Fan, Xiong Zheng, Huimin Wang, Zhenzhen Qin, Guangzhao Qin

npj Computational Materials· Volume 10· Issue 1

Abstract

The physical/chemical properties undergo significant transformations in the different states arising from phase transition. However, due to the lack of a dynamic perspective, transitional mesophases are largely underexamined, constrained by the high resource burden of first principles. Here, using molecular dynamics (MD) simulations empowered by the machine-learning potential, we proffer an innovative paradigm for phase transition: regulating the thermal transport properties via the transitional mesophase triggered by a uniaxial force field. We investigate the mechanical, electrical, and thermal transport properties of the two-dimensional carbon allotrope of Janus-graphene with strain-engineered phase transition. Notably, we found that the transitional mesophase significantly suppresses the thermal conductivity and induces strong anisotropy near the phase transition point. Through machine-learning-driven MD simulations, we achieved high-precision atomic-level simulations of Janus-graphene. The results show that thermal vibration-induced intermediate amorphous or interfacial phases induce strong and anisotropic interfacial thermal resistance. The investigation not only endows us with a novel perspective on mesophases during phase transitions but also enhances our holistic comprehension of the evolution of material properties.
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Research context

OpenAlex topics: Thermal properties of materials · Force Microscopy Techniques and Applications · Machine Learning in Materials Science

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References (57)

Show source-provided references
1. Kasprzak, M. et al. High-temperature silicon thermal diode and switch. Nano Energy 78, 105261 (2020).10.1016/j.nanoen.2020.105261 ↗
2. Zhang, Y., Wu, K. & Fu, Q. A structured phase change material with controllable thermoconductive highways enables unparalleled electricity via solar-thermal-electric conversion. Adv. Funct. Mater. 32, 2109255 (2022).10.1002/adfm.202109255 ↗
3. Li, S. et al. Anomalous thermal transport under high pressure in boron arsenide. Nature 612, 7940 (2022).10.1038/s41586-022-05381-x ↗
4. Zhou, Y., Dong, Z.-Y., Hsieh, W.-P., Goncharov, A. F. & Chen, X.-J. Thermal conductivity of materials under pressure. Nat. Rev. Phys. 4, 5 (2022).10.1038/s42254-022-00423-9 ↗
5. Liu, H., Qin, G., Lin, Y. & Hu, M. Disparate strain dependent thermal conductivity of two-dimensional penta-structures. Nano Lett. 16, 3831 (2016).10.1021/acs.nanolett.6b01311 ↗
6. Parrish, K. D., Jain, A., Larkin, J. M., Saidi, W. A. & McGaughey, A. J. Origins of thermal conductivity changes in strained crystals. Phys. Rev. B 90, 235201 (2014).10.1103/PhysRevB.90.235201 ↗
7. Lindsay, L. et al. Phonon thermal transport in strained and unstrained graphene from first principles. Phys. Rev. B 89, 155426 (2014).10.1103/PhysRevB.89.155426 ↗
8. Yuan, K., Zhang, X., Tang, D. & Hu, M. Anomalous pressure effect on the thermal conductivity of ZnO, GaN, and AlN from first-principles calculations. Phys. Rev. B 98, 144303 (2018).10.1103/PhysRevB.98.144303 ↗
9. Zhang, F. et al. Atomic-scale observation of reversible thermally driven phase transformation in 2D In2Se3. ACS Nano 13, 8004 (2019).10.1021/acsnano.9b02764 ↗
10. Lu, Q. et al. Bi-directional tuning of thermal transport in SrCoOx with electrochemically induced phase transitions. Nat. Mater. 19, 6 (2020).10.1038/s41563-020-0612-0 ↗
11. Ward, A. & Broido, D. A. Intrinsic phonon relaxation times from first-principles studies of the thermal conductivities of Si and Ge. Phys. Rev. B 81, 085205 (2010).10.1103/PhysRevB.81.085205 ↗
12. Omini, M. & Sparavigna, A. An iterative approach to the phonon Boltzmann equation in the theory of thermal conductivity. Phys. B: Condens. Matter 212, 101 (1995).10.1016/0921-4526(95)00016-3 ↗
13. Ward, A., Broido, D. A., Stewart, D. A. & Deinzer, G. Ab initio theory of the lattice thermal conductivity in diamond. Phys. Rev. B 80, 125203 (2009).10.1103/PhysRevB.80.125203 ↗
14. Li, W., Carrete, J., Katcho, N. A. & Mingo, N. ShengBTE: a solver of the Boltzmann transport equation for phonons. Comput. Phys. Commun. 185, 1747 (2014).10.1016/j.cpc.2014.02.015 ↗
15. Tadano, T., Gohda, Y. & Tsuneyuki, S. Anharmonic force constants extracted from first-principles molecular dynamics: applications to heat transfer simulations. J. Phys. Condens. Matter 26, 225402 (2014).10.1088/0953-8984/26/22/225402 ↗
16. Togo, A. First-principles phonon calculations with phonopy and phono3py. J. Phys. Soc. Jpn. 92, 012001 (2023).10.7566/JPSJ.92.012001 ↗
17. Barrat, J.-L., Baschnagel, J. & Lyulin, A. Molecular dynamics simulations of glassy polymers. Soft Matter 6, 3430 (2010).10.1039/b927044b ↗
18. Lin, F., Xiang, Y. & Shen, H.-S. Temperature dependent mechanical properties of graphene reinforced polymer nanocomposites—a molecular dynamics simulation. Compos. Part B: Eng. 111, 261 (2017).10.1016/j.compositesb.2016.12.004 ↗
19. Cheng, Y. et al. Magic angle in thermal conductivity of twisted bilayer graphene. Mater. Today Phys. 35, 101093 (2023).10.1016/j.mtphys.2023.101093 ↗
20. Mandal, S., Maity, I., Das, A., Jain, M. & Maiti, P. K. Tunable lattice thermal conductivity of twisted bilayer MoS 2. Phys. Chem. Chem. Phys. 24, 13860 (2022).10.1039/D2CP01304E ↗
21. Shi, J. et al. Double-shock compression pathways from diamond to BC8 carbon. Phys. Rev. Lett. 131, 146101 (2023).10.1103/PhysRevLett.131.146101 ↗
22. Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).10.1103/PhysRevLett.104.136403 ↗
23. Yu, L. et al. Janus graphene: a two-dimensional half-auxetic carbon allotrope with a nonchemical Janus configuration. Phys. Rev. B 109, L121402 (2024).10.1103/PhysRevB.109.L121402 ↗
24. Rowe, P., Deringer, V. L., Gasparotto, P., Csányi, G. & Michaelides, A. An accurate and transferable machine learning potential for carbon. J. Chem. Phys. 153, 034702 (2020).10.1063/5.0005084 ↗
25. Li, M., Cao, G., Luo, Y., Sheng, C. & Liu, H. Predicting the lattice thermal conductivity of alloyed compounds from the perspective of configurational entropy. Npj Comput Mater. 8, 75 (2022).10.1038/s41524-022-00771-1 ↗
26. Deringer, V. L., Caro, M. A. & Csányi, G. A general-purpose machine-learning force field for bulk and nanostructured phosphorus. Nat. Commun. 11, 5461 (2020).10.1038/s41467-020-19168-z ↗
27. Jinnouchi, R., Lahnsteiner, J., Karsai, F., Kresse, G. & Bokdam, M. Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on the fly with Bayesian inference. Phys. Rev. Lett. 122, 225701 (2019).10.1103/PhysRevLett.122.225701 ↗
28. Cui, C. et al. On-the-fly machine learning potential accelerated accurate prediction of lattice thermal conductivity of metastable silicon crystals. Phys. Rev. Mater. 7, 033803 (2023).10.1103/PhysRevMaterials.7.033803 ↗
29. Fan, Z. et al. GPUMD: a package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations. J. Chem. Phys. 157, 114801 (2022).10.1063/5.0106617 ↗
30. Friederich, P. et al. Machine learning dihydrogen activation in the chemical space surrounding Vaska’s complex. Chem. Sci. 11, 4584 (2020).10.1039/D0SC00445F ↗
31. Fan, Z., Dong, H., Harju, A. & Ala-Nissila, T. Homogeneous nonequilibrium molecular dynamics method for heat transport and spectral decomposition with many-body potentials. Phys. Rev. B 99, 064308 (2019).10.1103/PhysRevB.99.064308 ↗
32. Qian, C., McLean, B., Hedman, D. & Ding, F. A comprehensive assessment of empirical potentials for carbon materials. APL Mater. 9, 061102 (2021).10.1063/5.0052870 ↗
33. Li, R., Lee, E. & Luo, T. A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases. Mater. Today Phys. 12, 100181 (2020).10.1016/j.mtphys.2020.100181 ↗
34. Borlido, P., Steigemann, C., Lathiotakis, N. N., Marques, M. A. L. & Botti, S. Structural prediction of two-dimensional materials under strain. 2D Mater. 4, 045009 (2017).10.1088/2053-1583/aa85c6 ↗
35. Yu, L., Qin, Z., Wang, H., Zheng, X. & Qin, G. Half-negative Poisson’s ratio in graphene+ with intrinsic Dirac nodal loop. Cell Rep. Phys. Sci. 3, 100790 (2022).10.1016/j.xcrp.2022.100790 ↗
36. Yu, L. et al. Emerging negative Poisson’s ratio driven by strong intralayer interaction response in rectangular transition metal chalcogenides. Appl. Surf. Sci. 610, 155478 (2023).10.1016/j.apsusc.2022.155478 ↗
37. Wang, Y. et al. The consistent behavior of negative Poisson’s ratio with interlayer interactions. Mater. Adv. 3, 4334 (2022).10.1039/D2MA00118G ↗
38. Yu, L., Yan, Q. & Ruzsinszky, A. Negative Poisson’s ratio in 1T-type crystalline two-dimensional transition metal dichalcogenides. Nat. Commun. 8, 15224 (2017).10.1038/ncomms15224 ↗
39. Qin, Z., Qin, G. & Hu, M. Origin of anisotropic negative Poisson’s ratio in graphene. Nanoscale 10, 10365 (2018).10.1039/C8NR00696B ↗
40. Qin, G. & Qin, Z. Negative Poisson’s ratio in two-dimensional honeycomb structures. Npj Comput Mater. 6, 51 (2020).10.1038/s41524-020-0313-x ↗
41. Pan, J. et al. Auxetic two-dimensional transition metal selenides and halides. Npj Comput Mater. 6, 154 (2020).10.1038/s41524-020-00424-1 ↗
42. Jiang, J.-W. & Park, H. S. Negative Poisson’s ratio in single-layer black phosphorus. Nat. Commun. 5, 4727 (2014).10.1038/ncomms5727 ↗
43. Kubo, R. Statistical-mechanical theory of irreversible processes. I. General theory and simple applications to magnetic and conduction problems. J. Phys. Soc. Jpn. 12, 570 (1957).10.1143/JPSJ.12.570 ↗
44. Green, M. S. Markoff random processes and the statistical mechanics of time‐dependent phenomena. ii. irreversible processes in fluids. J. Chem. Phys. 22, 398 (2004).10.1063/1.1740082 ↗
45. Song, Q. & Chen, G. Evaluation of the diffuse mismatch model for phonon scattering at disordered interfaces. Phys. Rev. B 104, 085310 (2021).10.1103/PhysRevB.104.085310 ↗
46. Lumpkin, M. E., Saslow, W. M. & Visscher, W. M. One-dimensional Kapitza conductance: Comparison of the phonon mismatch theory with computer experiments. Phys. Rev. B 17, 4295 (1978).10.1103/PhysRevB.17.4295 ↗
47. Toh, C.-T. et al. Synthesis and properties of free-standing monolayer amorphous carbon. Nature 577, 199 (2020).10.1038/s41586-019-1871-2 ↗
48. Fan, Q. et al. Biphenylene network: a nonbenzenoid carbon allotrope. Science 372, 852 (2021).10.1126/science.abg4509 ↗
49. Hou, L. et al. Synthesis of a monolayer fullerene network. Nature 606, 507 (2022).10.1038/s41586-022-04771-5 ↗
50. Yang, L. et al. Suppressed thermal transport in silicon nanoribbons by inhomogeneous strain. Nature 629, 1021 (2024).10.1038/s41586-024-07390-4 ↗
51. Kresse, G. & Hafner, J. Ab inito molecular dynamics for metals. Phys. Rev. B 47, 558 (1993).10.1103/PhysRevB.47.558 ↗
52. Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758 (1999).10.1103/PhysRevB.59.1758 ↗
53. Han, Z., Yang, X., Li, W., Feng, T. & Ruan, X. FourPhonon: an extension module to ShengBTE for computing four-phonon scattering rates and thermal conductivity. Comput. Phys. Commun. 270, 108179 (2022).10.1016/j.cpc.2021.108179 ↗
54. Yu, L.-F. et al. Realizing ultra-low thermal conductivity by strong synergy of asymmetric geometry and electronic structure in boron nitride and arsenide. Rare Met 42, 210 (2023).10.1007/s12598-022-02187-8 ↗
55. Wei, D.-H. et al. Insight into vertical piezoelectric characteristics regulated thermal transport in van der Waals two-dimensional materials. Rare Met. 43, 770 (2023).10.1007/s12598-023-02368-z ↗
56. Fan, Z. et al. Neuroevolution machine learning potentials: combining high accuracy and low cost in atomistic simulations and application to heat transport. Phys. Rev. B 104, 104309 (2021).10.1103/PhysRevB.104.104309 ↗
57. Fan, Z., Chen, W., Vierimaa, V. & Harju, A. Efficient molecular dynamics simulations with many-body potentials on graphics processing units. Comput. Phys. Commun. 218, 10 (2017).10.1016/j.cpc.2017.05.003 ↗