Perspective: Web based machine learning models for real time screening of thermoelectric materials properties
The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structural families, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools such as density functional theory (DFT) offer the possibility of rationally guiding experimental synthesis efforts toward very different chemistries. However, in practice, predicting thermoelectric properties from first principles remains a challenging endeavor [J. Carrete et al. Phys. Rev. X 4, 011019 (2014) and experimental researchers generally do not directly use computation to drive their own synthesis efforts. To bridge this practical gap between experimental needs and computational tools, we report an open machine learning based recommendation engine (http:/thermoelectrics.citrination.com) for materials researchers that suggests promising new thermoelectric compositions based on pre screening about 25 000 known materials and also evaluates the feasibility of use.