Multifunctional structural design of graphene thermoelectrics by Bayesian optimization.

Materials development often confronts a dilemma as it needs to satisfy multifunctional, often conflicting, demands. For example, thermoelectric conversion requires high electrical conductivity, a high Seebeck coefficient, and low thermal conductivity, despite the fact that these three properties are normally closely correlated. Nanostructuring techniques have been shown to break the correlations to some extent; however, optimal design has been a major challenge due to the extraordinarily large degrees of freedom in the structures.