| Abstract Scope |
Artificial intelligence (AI) studies to predict or suggest efficient thermoelectric materials have become increasingly important. We developed a machine learning pipeline trained with multivariable inputs on the massive public Starrydata2 database to predict all thermoelectric key properties, including the figure of merit zT. Due to the inclusion of this massive dataset, our model presents a promising possibility to further expand the understanding of the selection of features with various thermoelectric materials. Among the several supervised ML models implemented, eXtreme gradient boosting algorithms (XGBoost) was revealed to be the best on five-fold validations, closely followed by LightGBM. Additionally, with the aid of feature selection and importance analysis, useful chemical features were identified to be most impactful. As I will demonstrate, we are now in the exciting situation to predict the performance simply based on the chemical formula. |