Machine learning based feature engineering for thermoelectric materials by design

Availability of material datasets through high performance computing has enabled the use of machine learning to not only discover correlations and employ materials informatics to perform screening, but also to take the first steps towards materials by design. Computational materials databases are well-labelled and provide a fertile ground for predicting both ground-state and functional properties of materials. However, a clear design approach that allows prediction of materials with the desired functional performance does not yet exist. In this work, we train various machine learning models on a dataset curated from a combination of Materials Project as well as computationally calculated thermoelectric electronic power factor using a constant relaxation time Boltzmann transport equation (BoltzTrap). We show that simple random forest-based machine learning models outperform more complex neural network-based approaches on the moderately sized dataset and also allow for interpretability. In addition, when trained on only cubic material systems, the best performing machine learning model employs a perturbative scanning approach to find new candidates in Materials Project that it has never seen before, and automatically converges upon half-Heusler alloys as promising thermoelectric materials. We validate this prediction by performing density functional theory and BoltzTrap calculations to reveal accurate matching. One of those predicted to be a good material, NbFeSb, has been studied recently by the thermoelectric community; from this study, we propose four new half-Heusler compounds as promising thermoelectric materials - TiGePt, ZrInAu, ZrSiPd and ZrSiPt. Our approach is generalizable to extrapolate into previously unexplored material spaces and establishes an automated pipeline for the development of high-throughput functional materials. We train several machine learning models on a dataset comprised by Materials Project and calculated thermoelectric power factor. We show that a random forest model outperforms more complex approaches for the dataset and allows for interpretability.