Npj Comput. Mater.: 超低晶格热导率材料—机器学习加速寻找
研究材料的热传输性质，是在各种热能管理平台上寻找和部署合适材料的关键，如热障涂层、废热回收设备和需要快速散热的现代高性能计算架构等等。这个方向的一个持续的研究热点，是寻找具有极端热传输特性的材料。其中，具有极低晶格热导率 (κl) 的半导体材料很有希望能在热电学中得到应用，可将热能转化为电能。然而，如何发现以及合成新的化合物是材料科学中一项常重要但又极具挑战性的难题。传统上实验室都是采用试错法来合成新的化合物，但这种方法成本高、效率低。近年来，精确的量子力学方法、高通量密度泛函理论和机器学习等现代计算方法的发展，大大加速了新化合物的开发。
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′Q3 (A/M/M' = alkali, alkaline earth, post-transition metals, lanthanides, and Q = chalcogens). These compounds are shown to possess ultralow lattice thermal conductivity (κl), a desired requirement for thermal-barrier coatings and thermoelectrics. Upon screening the thermodynamic stability of ~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 κl using Peierls–Boltzmann transport equation, which reveals ultralow κl (<2 Wm−1K−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.
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