Bayesian optimization of layered transition metal dichalcogenide hetero-structures

Vertical hetero-structures made from stacked monolayers of transition metal dichalcogenides (TMDC) are promising candidates for the next generation optoelectronic and thermoelectric devices. Identification of optimal layered materials for specific applications requires estimation of several physical properties, including electronic band structure and thermal transport coefficients. However, exhaustive screening of the material structure space using ab initio calculations is currently outside the bounds of existing computational resources. Furthermore, the functional form of how each physical property relates to the structures is often unknown, making gradient-based optimization unsuitable. Here, we present a model based on the Bayesian optimization to expedite the discovery of optimal N-layered hetero-structures. As specific exmaples, we consider the electronic band gap and thermoelectric figure of merit. With high probability, the Bayesian optimization discovered the optimal hetero-structure after evaluation of only ~15% of all possible 3- or 4-layered structures.