Data-Driven Discovery and Design of Thermoelectric Materials

Thermoelectrics have potentially significant energy applications, but only if high efficiency materials can be found. However, discovery and design of novel thermoelectrics is particularly challenging, due to the complex set of materials properties that must be simultaneously optimized. Data-driven approaches to discovery and design of materials are a research area that has the potential to significantly accelerate discovery of these energy materials. Here we discuss our efforts at developing and applying data-driven computational techniques that enable an accelerated discovery of novel thermoelectrics. These techniques involve a combination of high-throughput density functional theory (DFT) calculations, inverse design approaches, and machine learning and artificial intelligence based methods. We discuss several recent examples of these methods: (i) inverse design strategies based on a materials database screening to design a solid with a desired band structure, specifically both flat and dispersive components with respect to crystal momentum, (ii) inverse design strategies to identify compounds with ultralow thermal conductivity (iii) an effective strategy of weakening interatomic interactions and therefore suppressing lattice thermal conductivity based on chemical bonding principles, and (iv) the development of crystal graph based neural network techniques to accelerate high-throughput computational screening for materials with ultralow thermal conductivity.