Advances in materials informatics (MI), which combines material property calculations/measurements and informatics algorithms, have realized properties in the nanostructures of thermal functional materials beyond what is accessible using empirical approaches based on physical instincts and models. In this Tutorial, we introduce technological procedures and underlying knowledge of MI combining thermal transport calculations and machine learning using an optimization problem of superlattice structures as an example (sample script available in the supplement). To provide fundamental guidance on how to use MI, we describe practical details about descriptors, objective functions, property calculators, machine learning (Bayesian optimization) algorithms, and optimization efficiencies. We then briefly review the recent successful applications of MI to design thermoelectric and thermal radiation materials. Finally, we summarize and provide future perspectives about the topic.

中文翻译：

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通过结合热传输计算和机器学习来设计热功能材料

结合材料特性计算/测量和信息学算法的材料信息学 (MI) 的进步已经实现了热功能材料纳米结构的特性，超出了使用基于物理本能和模型的经验方法所能获得的特性。在本教程中，我们以超晶格结构的优化问题为例，结合热传输计算和机器学习，介绍了 MI 的技术程序和基础知识（示例脚本在补充文件中提供）。为了提供有关如何使用 MI 的基本指导，我们描述了有关描述符、目标函数、属性计算器、机器学习（贝叶斯优化）算法和优化效率的实用细节。然后我们简要回顾了最近 MI 在设计热电和热辐射材料方面的成功应用。最后，我们总结并提供有关该主题的未来观点。