Quantitative descriptions of the structure-thermal property correlation have always been a challenging bottleneck in designing functional materials with superb thermal properties. In the past decade, the first-principles-based modeling of phonon properties using density functional theory and the Boltzmann transport equation has become a common practice for predicting the thermal conductivity of new materials. However, first-principles calculations of thermal properties are too costly for high-throughput material screening and multi-scale structural design. First-principles calculations also face several fundamental challenges in modeling thermal transport properties, for example, of crystalline materials with defects, of amorphous materials, and for materials at high temperatures. In the past five years or so, machine learning started to play a role in solving the aforementioned challenges. This review provides a comprehensive summary and discussion on the state-of-the-art, future opportunities, and the remaining challenges in implementing machine learning techniques for studying thermal conductivity. After a brief introduction to the working principles of machine learning algorithms and descriptors for characterizing material structures, recent research using machine learning to study nanoscale thermal transport is discussed. Three major applications of machine learning techniques for predicting thermal properties are discussed. First, machine learning is applied to solve the challenges in modeling phonon transport of crystals with defects, in amorphous materials, and at high temperatures. In particular, machine learning is used to build high-fidelity interatomic potentials to bridge the gap between first-principles calculations and empirical molecular dynamics simulations. Second, machine learning can be used to study the correlation between thermal conductivity and other relevant properties for the high-throughput screening of functional materials. Finally, machine learning is a powerful tool for structural design to achieve target thermal conductance or thermal conductivity. This review concludes with a summary and outlook for future directions for implementing machine learning in thermal sciences.

结构-热性能相关性的定量描述一直是设计具有优异热性能的功能材料的一个具有挑战性的瓶颈。在过去的十年中，使用密度泛函理论和玻尔兹曼传输方程基于第一性原理的声子特性建模已成为预测新材料热导率的常见做法。然而，热性能的第一性原理计算对于高通量材料筛选和多尺度结构设计来说成本太高。第一性原理计算在模拟热传输特性时也面临几个基本挑战，例如，具有缺陷的晶体材料、非晶材料和高温材料。在过去五年左右的时间里，机器学习开始在解决上述挑战方面发挥作用。这篇综述全面总结和讨论了在实施机器学习技术研究热导率方面的最新技术、未来机会和剩余挑战。在简要介绍了机器学习算法的工作原理和表征材料结构的描述符之后，讨论了最近使用机器学习来研究纳米级热传输的研究。讨论了机器学习技术在预测热性能方面的三个主要应用。首先，机器学习用于解决在模拟具有缺陷的晶体、非晶材料和高温下的声子传输方面的挑战。特别是，机器学习用于构建高保真原子间势，以弥合第一性原理计算和经验分子动力学模拟之间的差距。其次，机器学习可用于研究热导率与其他相关特性之间的相关性，用于功能材料的高通量筛选。最后，机器学习是实现目标热导率或热导率的结构设计的强大工具。本综述总结了在热科学中实施机器学习的未来方向的总结和展望。机器学习可用于研究热导率与其他相关特性之间的相关性，用于功能材料的高通量筛选。最后，机器学习是实现目标热导率或热导率的结构设计的强大工具。本综述总结了在热科学中实施机器学习的未来方向的总结和展望。机器学习可用于研究热导率与其他相关特性之间的相关性，用于功能材料的高通量筛选。最后，机器学习是实现目标热导率或热导率的结构设计的强大工具。本综述总结了在热科学中实施机器学习的未来方向的总结和展望。