论文标题
图表无机晶体的晶格导热率
Charting Lattice Thermal Conductivity of Inorganic Crystals
论文作者
论文摘要
导热率是一种基本的材料特性，但要预测的挑战性，在$ 10^5 $中，综合的无机材料中只有不到5％。在这项工作中，我们通过结合图神经网络和随机森林方法来提取控制晶格导热率的结构化学。我们表明，单位细胞构型特性（例如原子体积和键长）的平均值和变化是最重要的特征，其次是质量和元素电负性。我们将晶格热导率的结构化学绘制到扩展的Van-Arkel三角形中，并预测无机晶体结构数据库中所有已知无机材料的热导率。对于后者，我们为其他应用程序开发了一个可扩展的转移学习框架。
Thermal conductivity is a fundamental material property but challenging to predict, with less than 5% out of about $10^5$ synthesized inorganic materials being documented. In this work, we extract the structural chemistry that governs lattice thermal conductivity, by combining graph neural networks and random forest approaches. We show that both mean and variation of unit-cell configurational properties, such as atomic volume and bond length, are the most important features, followed by mass and elemental electronegativity. We chart the structural chemistry of lattice thermal conductivity into extended van-Arkel triangles, and predict the thermal conductivity of all known inorganic materials in the Inorganic Crystal Structure Database. For the latter, we develop a transfer learning framework extendable for other applications.
