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

doi:10.1007/s11467-022-1170-5

Front. Phys.

2022, Vol. 17

Issue (5) : 53507 https://doi.org/10.1007/s11467-022-1170-5

RESEARCH ARTICLE

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

Cuiqian Yu, Yulou Ouyang, Jie Chen(

)

Center for Phononics and Thermal Energy Science, China−EU Joint Lab for Nanophononics, MOE Key Laboratory of Advanced Micro-structured Materials, School of Physics Science and Engineering, Tongji University, Shanghai 200092, China

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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.

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

Corresponding Author(s): Jie Chen

Issue Date: 17 June 2022

Cite this article:

Cuiqian Yu,Yulou Ouyang,Jie Chen. Enhancing thermal transport in multilayer structures: A molecular dynamics study on Lennard−Jones solids[J]. Front. Phys. , 2022, 17(5): 53507.

URL:

https://academic.hep.com.cn/fop/EN/10.1007/s11467-022-1170-5
https://academic.hep.com.cn/fop/EN/Y2022/V17/I5/53507

Fig.1 Schematic graphs for the three types of multilayer structures and NEMD setup. The dark and light regions represent material A and B, respectively. (a) SL structure with period length of P₀ and number of layers of N₀. (b) 2SL structure combined by two SL structures with different period lengths (P₁ and P₂) and number of layers (N₁ and N₂). R₁ is the ratio of the left region. (c) RML structure. (d) Schematic setup for the NEMD simulations.

Fig.2 The relationship between the ratio of the left region R₁ in 2SL and

κ e f f

of multilayer structures with that the P₀ of the corresponding SL is (a) 8 UC, (b) 4 UC, (c) 16 UC and (d) 16 UC. TheP₁ of 2SL in (a), (b), (c) is smaller than the transition period lengthP_t= 8 UC, while P₁ in (d) is larger thanP_t. The dashed lines in each plot denote the thermal conductivity of corresponding SL with the same interface density. Here T_h= 35 K and T_c= 25 K are used in the simulations.

Fig.3 The transmission spectra of a typical set of SL, 2SL and RML. The configurations of the three structures are set as follows: P₀ = 8 UC for SL structure, and P₁ = 2 UC, N₁ = 255, P₂ = 1538 UC, N₂ = 2 for 2SL and RML structures. Here T_h= 35 K and T_c= 25 K are used in the simulations.

Fig.4 The relationship between the ratio R₁ and

κ e f f

in a particular set of 2SL and RML multilayer structures at various temperatures. Here P₀= 8 UC and P₁= 2 UC are used in the simulations, and the temperature denotes the average temperature of two heat baths. The

κ e f f

of corresponding SL is 3.86 W·m⁻¹·K⁻¹ at 30 K, 3.54 W·m⁻¹·K⁻¹ at 50 K, and 3.06 W·m⁻¹·K⁻¹ at 80 K. The maximum enhancement ratio

Δ

for the optimal structure is 193% at 30 K, 157% at 50 K, and 134% at 80 K.

Fig.5 GBDT prediction of

κ e f f

of 2SL and RML structures for (a) P₁ < 8 UC, and (b) P₁ > 8 UC. The structure id is arranged in order of increasing corresponding P₀ for better visualization of the data.

P₀(UC)	$κ e f f$ , SL (W·m⁻¹·K⁻¹)	P₁ (UC)	P₂(UC)	$κ e f f$ , 2SL (W·m⁻¹·K⁻¹)	Maximum $Δ$ (%)

4	4.25	2	1026	11.11	161
8	3.86	2	1538	11.30	193
16	4.32	2	1794	11.71	171
16	4.32	12	524	6.84	58
32	4.99	2	1922	11.65	134
32	4.99	10	1418	10.20	104
64	5.33	2	1986	11.56	117
64	5.33	16	1552	10.26	93
128	6.87	2	2018	11.55	68
128	6.87	18	1778	10.24	49
256	8.77	2	2034	11.55	32
256	8.77	16	1936	10.81	23
512	11.64	2	2042	11.84	2
512	11.64	368	560	12.30	6
1024	15.01	2	2046	12.39	−17
1024	15.01	1022	1026	15.01	0

Tab.1 The machine learning predictions of

κ e f f

in SL structure and the optimal 2SL structure with different configurations (P₁ < 8 UC and P₁ > 8 UC) versus the period P₀of SL structure. Here,

Δ

is the relative enhancement ratio defined as

Δ = κ e f f 2 S L − κ e f f S L κ e f f S L

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