Machine Learning Approach for Maximizing Thermoelectric Properties of BiCuSeO and Discovering New Doping Element

Abstract

Machine learning (ML) has increasingly received interest as a new approach to accelerating development in materials science. It has been applied to thermoelectric materials research for discovering new materials and designing experiments. Generally, the amount of data in thermoelectric materials research, especially experimental data, is very small leading to an undesirable ML model. In this work, the ML model for predicting ZT of the doped BiCuSeO was implemented. The method to improve the model was presented step-by-step. This included normalizing the experimental ZT of the doped BiCuSeO with the pristine BiCuSeO, selecting data for the BiCuSeO doped at Bi-site only, and limiting important features for the model construction. The modified model showed significant improvement, with the R2 of 0.93, compared to the original model (R2 of 0.57). The model was validated and used to predict the ZT of the unknown doped BiCuSeO compounds. The predicted result was logically justified based on the thermoelectric principle. It means that the ML model can guide the experiments to improve the thermoelectric properties of BiCuSeO and can be extended to other materials.