Wu Yidan, Song Dongxing, An Meng, Chi Cheng, Zhao Chunyu, Yao Bing, Ma Weigang, Zhang Xing
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
Key Laboratory of Process Heat Transfer and Energy Saving of Henan Province, School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450001, China.
Natl Sci Rev. 2024 Nov 23;12(1):nwae411. doi: 10.1093/nsr/nwae411. eCollection 2025 Jan.
The high thermopower of ionic thermoelectric (-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 -TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an 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 -TE field, offering significant promise for accelerating the discovery and development of high-performance -TE materials.
离子热电（-TE）材料的高热电势有望应用于小型化废热回收装置和热传感器。然而，由于缺乏理论支撑的繁琐试错实验，进展受到阻碍。在此，通过引入简化的分子输入线性条目系统，我们解决了-TE材料类型不一致带来的挑战，并提出了一种机器学习模型，该模型在测试数据集上评估塞贝克系数的相关系数为0.98。使用这个工具，我们通过实验确定了一种塞贝克系数为41.39 mV/K的水性聚氨酯/碘化钾离子凝胶。此外，可解释分析表明，可旋转键的数量和离子的正辛醇-水分配系数对塞贝克系数有负面影响，分子动力学模拟证实了这一点。这种机器学习辅助框架是-TE领域的开创性努力，为加速高性能-TE材料的发现和开发提供了巨大希望。
