Multifunctional structural design of graphene thermoelectrics by Bayesian optimization.

ABSTRACT: 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. By taking graphene nanoribbons (GNRs) as a representative thermoelectric material, we carried out structural optimization by alternating multifunctional (phonon and electron) transport calculations and Bayesian optimization to resolve the trade-off. As a result,

SUBMITTER: Yamawaki M 

PROVIDER: S-EPMC6003749 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature