Traditional simulation methods have made prominent progress in aiding experiments for understanding thermal transport properties of materials, and in predicting thermal conductivity of novel materials. However, huge challenges are also encountered when exploring complex material systems, such as formidable computational costs. As a rising computational method, machine learning has a lot to offer in this regard, not only in speeding up the searching and optimization process, but also in providing novel perspectives. In this work, we review the state-of-the-art studies on material’s thermal properties based on machine learning technique. First, the basic principles of machine learning method are introduced. We then review applications of machine learning technique in the prediction and optimization of material’s thermal properties, including thermal conductivity and interfacial thermal resistance. Finally, an outlook is provided for the future studies.

传统的模拟方法在帮助实验以了解材料的热传输特性以及预测新型材料的热导率方面取得了显着进展。但是，在探索复杂的材料系统时也会遇到巨大的挑战，例如巨大的计算成本。作为一种新兴的计算方法，机器学习不仅可以加快搜索和优化过程的速度，而且可以提供新颖的观点，在这方面可以提供很多帮助。在这项工作中，我们回顾了基于机器学习技术的材料热性能的最新研究。首先，介绍了机器学习方法的基本原理。然后，我们回顾了机器学习技术在预测和优化材料热性能方面的应用，包括热导率和界面热阻。最后，为将来的研究提供了展望。