Unlocking new possibilities in ionic thermoelectric materials:a machine learning perspective  

作　　者：Yidan Wu Dongxing Song Meng An Cheng Chi Chunyu Zhao Bing Yao Weigang Ma Xing Zhang 

机构地区：[1]Key Laboratory for Thermal Science and Power Engineering of Ministry of Education,Department of Engineering Mechanics,Tsinghua University,Beijing 100084,China [2]Key Laboratory of Process Heat Transfer and Energy Saving of Henan Province,School of Mechanics and Safety Engineering,Zhengzhou University,Zhengzhou 450001,China [3]College of Mechanical and Electrical Engineering,Shaanxi University of Science and Technology,Xi’an 710021,China [4]Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education,School of Energy Power and Mechanical Engineering,North China Electric Power University,Beijing 102206,China [5]School of Materials and Chemical Engineering,Xuzhou University of Technology,Xuzhou 221018,China

出　　处：《National Science Review》2025年第1期189-200,共12页国家科学评论(英文版)

基　　金：supported by the Tsinghua-Toyota Joint Research Fund,the National Natural Science Foundation of China(52176078 and 52250273);the Tsinghua University Initiative Scientific Research Program.

摘　　要：The high thermopower of ionic thermoelectric(i-TE)materials holds promise for miniaturized waste-heat recovery devices and thermal sensors.However,progress is hampered by laborious trial-and-error experimentations,which lack theoretical underpinning.Herein,by introducing the simplified molecular-input line-entry system,we have addressed the challenge posed by the inconsistency of i-TE material types,and present a machine learning model that evaluates the Seebeck coefficient with an R^(2) of 0.98 on the test dataset.Using this tool,we experimentally identify a waterborne polyurethane/potassium iodide ionogel with a Seebeck coefficient of 41.39 mV/K.Furthermore,interpretable analysis reveals that the number of rotatable bonds and the octanol-water partition coefficient of ions negatively affect Seebeck coefficients,which is corroborated by molecular dynamics simulations.This machine learning-assisted framework represents a pioneering effort in the i-TE field,offering significant promise for accelerating the discovery and development of high-performance i-TE materials.

关 键 词：thermoelectric conversion ionic thermoelectric materials machine learning interpretable analysis 

分 类 号：O47[理学—半导体物理]