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

ABSTRACT: 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 p

SUBMITTER: Wu Y 

PROVIDER: S-EPMC11702661 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature