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

The high thermopower of ionic thermoelectric (<i>i</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>i</i>-TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an <i>R</i> 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>i</i>-TE field, offering significant promise for accelerating the discovery and development of high-performance <i>i</i>-TE materials.