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arXiv 2109.03751cond-mat.mtrl-sci
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Scale-invariant Machine-learning Model Accelerates the Discovery of Quaternary Chalcogenides with Ultralow Lattice Thermal Conductivity

Koushik Pal, Cheol Woo Park, Yi Xia, Jiahong Shen, Chris Wolverton

英文摘要:

Intrinsically low lattice thermal conductivity ($κ_l$) is a desired requirement in many crystalline solids such as thermal barrier coatings and thermoelectrics. Here, we design an advanced machine-learning (ML) model based on crystal graph convolutional neural network that is insensitive to volumes (i.e., scale) of the input crystal structures to discover novel quaternary chalcogenides, AMM'Q$_3$ (A/M/M'=alkali, alkaline-earth, post-transition metals, lanthanides, Q=chalcogens). Upon screening the thermodynamic stability of $\sim$ 1 million compounds using the ML model iteratively and performing density functional theory (DFT) calculations for a small fraction of compounds, we discover 99 compounds that are validated to be stable in DFT. Taking several DFT-stable compounds, we calculate their $κ_l$ using phonon-Boltzmann transport equation, which reveals ultralow-$κ_l$ ($<$ 2 Wm$^{-1}$K$^{-1}$ at room-temperature) due to their soft elasticity and strong phonon anharmonicity. Our work demonstrates the high-efficiency of scale-invariant ML model in predicting novel compounds and presents experimental research opportunities with these new compounds.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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