Thermal conductivity decomposition in two-dimensional materials: Application to graphene
By Zheyong Fan, Luiz Felipe Cavalcanti Pereira, Petri Hirvonen, M. Ervasti, K. Elder, Davide Donadio, T. Ala‐Nissila, Ari Harju. 2016
134 citations recorded by the source corpus.
Two-dimensional materials have unusual phonon spectra due to the presence of flexural (out-of-plane) modes. Although molecular dynamics simulations have been extensively used to study heat transport in such materials, conventional formalisms treat the phonon dynamics isotropically. Here, we decompose the microscopic heat current in atomistic simulations into in-plane and out-of-plane components, corresponding to in-plane and out-of-plane phonon dynamics, respectively. This decomposition allows for direct computation of the corresponding thermal conductivity components in two-dimensional materials. We apply this decomposition to study heat transport in suspended graphene, using both equilibrium and nonequilibrium molecular dynamics simulations. We show that the flexural component is responsible for about two-thirds of the total thermal conductivity in unstrained graphene, and the acoustic flexural component is responsible for the logarithmic divergence of the conductivity when a sufficiently large tensile strain is applied.
Subfields: Civil and Structural Engineering, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/38b6a862d13818751ae2d3680ac163cddba366d3
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In Civil and Structural Engineering
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- Baowen Li
- Xiaobo Yin
- Yujie Wei
- Ronggui Yang
- Stéphane P.A. Bordas
In Materials Chemistry
- Xiaobo Yin
- Yujie Wei
- Xiaokun Gu
- Baowen Li
- Ronggui Yang
- Xiaoying Zhuang
- Timon Rabczuk
- Bohayra Mortazavi
Citations in the corpus
- Colloquium: Phononic thermal properties of two-dimensional materials (2017, methodology): “On the other hand, strain engineering has also been proposed to tune electronic, photonic, and even thermal properties (Mohiuddin et al., 2009; Conley et al., 2013; Fan et al., 2017).”
- Outstanding strength, optical characteristics and thermal conductivity of graphene-like BC3 and BC6N semiconductors (2019, result): “arkably high for monolayers. For reference, the room temperature thermal conductivity of bulk copper is 400 W/m.K, while for hexagonal BN it is 600 W/m.K [76], and for graphene we have 2900 100 W/m.K [77]. It 19 is worth of notice that we have previously observed a strong correspondence between anis”
- Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport (2021, unknown): “Therefore, using a single V100 GPU with 32 gigabytes of device memory, one can simulate systems with up to a few million atoms using the NEP potential in gpumd .”