Superfolded configuration induced low thermal conductivity in two-dimensional carbon allotropes revealed via machine learning force constant potential
Linfeng Yu, Kexin Dong, Qi Yang, Yi Zhang, Xiong Zheng, Huimin Wang,, Zhenzhen Qin, and Guangzhao Qin

TL;DR
This study uses machine learning and phonon transport calculations to reveal how superfolded structures in 2D carbon materials drastically reduce their thermal conductivity, providing insights for designing materials with tailored heat transport properties.
Contribution
It introduces a novel approach combining machine learning force constants with phonon transport theory to analyze the impact of folding structures on thermal conductivity in 2D carbon allotropes.
Findings
Folded structures cause symmetry breaking and suppress phonon velocities.
Strong phonon scattering leads to low thermal conductivity.
Mechanisms of in-plane and out-of-plane folding influence heat transport.
Abstract
Understanding the fundamental link between structure and functionalization is crucial for the design and optimization of functional materials, since different structural configurations could trigger materials to demonstrate diverse physical, chemical, and electronic properties. However, the correlation between crystal structure and thermal conductivity (\k{appa}) remains enigmatic. In this study, taking two-dimensional (2D) carbon allotropes as study cases, we utilize phonon Boltzmann transport equation (BTE) along with machine learning force constant potential to thoroughly explore the complex folding structure of pure sp2 hybridized carbon materials from the perspective of crystal structure, mode-level phonon resolved thermal transport, and atomic interactions, with the goal of identifying the underlying relationship between 2D geometry and \k{appa}. We propose two potential structure…
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Taxonomy
TopicsThermal properties of materials · Advanced Thermoelectric Materials and Devices · Machine Learning in Materials Science
