Deng Tingting, Qiu Pengfei, Yin Tingwei, Li Ze, Yang Jiong, Wei Tianran, Shi Xun
School of Chemistry and Materials Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, 200050, China.
Adv Mater. 2024 Mar;36(13):e2311278. doi: 10.1002/adma.202311278. Epub 2024 Jan 12.
Searching for new high-performance thermoelectric (TE) materials that are economical and environmentally friendly is an urgent task for TE society, but the advancements are greatly limited by the time-consuming and high cost of the traditional trial-and-error method. The significant progress achieved in the computing hardware, efficient computing methods, advance artificial intelligence algorithms, and rapidly growing material data have brought a paradigm shift in the investigation of TE materials. Many electrical and thermal performance descriptors are proposed and efficient high-throughput (HTP) calculation methods are developed with the purpose to quickly screen new potential TE materials from the material databases. Some HTP experiment methods are also developed which can increase the density of information obtained in a single experiment with less time and lower cost. In addition, machine learning (ML) methods are also introduced in thermoelectrics. In this review, the HTP strategies in the discovery of TE materials are systematically summarized. The applications of performance descriptor, HTP calculation, HTP experiment, and ML in the discovery of new TE materials are reviewed. In addition, the challenges and possible directions in future research are also discussed.
寻找经济环保的新型高性能热电（TE）材料是热电领域的一项紧迫任务，但传统试错法耗时且成本高昂，极大地限制了该领域的进展。计算硬件、高效计算方法、先进人工智能算法的显著进步以及快速增长的材料数据，给热电材料研究带来了范式转变。人们提出了许多电学和热学性能描述符，并开发了高效的高通量（HTP）计算方法，以便从材料数据库中快速筛选出新的潜在热电材料。还开发了一些HTP实验方法，这些方法能够以更少的时间和更低的成本增加单次实验获得的信息密度。此外，机器学习（ML）方法也被引入到热电领域。在这篇综述中，系统总结了发现热电材料的HTP策略。综述了性能描述符、HTP计算、HTP实验和ML在发现新型热电材料中的应用。此外，还讨论了未来研究中的挑战和可能的方向。
