Assessing thermoelectric performance of quasi 0D carbon and polyaniline nanocomposites using machine learning

AbstractThermoelectric materials have been widely recognized as a simple approach to harness green energy by converting thermal gradients into electrical energy. However, the intricate interplay between electrical conductivity, Seebeck coefficient, and thermal conductivity in thermoelectric materials presents a challenge to improving their efficiency. Traditional experimental methods and calculation methods have troublesome steps and long cycles for predicting new thermoelectric materials. In this work we present materials informatics-based approach, where statistical and machine learning models like correlation matrix, multiple linear regression, principal component analysis and artificial neural network were employed to find the relationship between features and thermoelectric performance. Furthermore, artificial neural network was used to analyze the roles of several compositional and microstructural features along with temperature on electrical conductivity, thermal conductivity, Seebeck coefficient and thermoelectric figure of merit (ZT) for PANI and quasi 0D carbon-based composites.Keywords: Nanocompositespolyanilinecarbon nanostructuresmaterials informaticsthermoelectric performancemachine learningdata analytics AcknowledgmentsMR acknowledges the support of "University of Alberta-Tecnologico de Monterrey Seed Fund, 2022-2023" and SNI fellowship awarded by CONACyT (Grant ID 1047863).Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementThe data can be requested and accessed from the authors.