Materials Genome Institute, Shanghai University, Shanghai200444, China.
School of Physics and Electronic Science, East China Normal University, Shanghai200241, China.
J Phys Chem Lett. 2023 Feb 23;14(7):1808-1822. doi: 10.1021/acs.jpclett.2c03073. Epub 2023 Feb 10.
Thermoelectric (TE) materials can directly convert heat to electricity and vice versa and have broad application potential for solid-state power generation and refrigeration. Over the past few decades, efforts have been made to develop new TE materials with high performance. However, traditional experiments and simulations are expensive and time-consuming, limiting the development of new materials. Machine learning (ML) has been increasingly applied to study TE materials in recent years. This paper reviews the recent progress in ML-based TE material research. The application of ML in predicting and optimizing the properties of TE materials, including electrical and thermal transport properties and optimization of functional materials with targeted TE properties, is reviewed. Finally, future research directions are discussed.
热电 (TE) 材料可以直接将热能转换为电能，反之亦然，在固态发电和制冷方面具有广泛的应用潜力。在过去的几十年中，人们一直在努力开发具有高性能的新型 TE 材料。然而，传统的实验和模拟既昂贵又耗时，限制了新材料的发展。近年来，机器学习 (ML) 越来越多地应用于 TE 材料的研究。本文综述了基于机器学习的 TE 材料研究的最新进展。综述了 ML 在预测和优化 TE 材料性能，包括电输运和热输运性能，以及优化具有目标 TE 性能的功能材料方面的应用。最后，讨论了未来的研究方向。
