Quantitative prediction of grain boundary thermal conductivities from local atomic environments.

ABSTRACT: Quantifying the dependence of thermal conductivity on grain boundary (GB) structure is critical for controlling nanoscale thermal transport in many technologically important materials. A major obstacle to determining such a relationship is the lack of a robust and physically intuitive structure descriptor capable of distinguishing between disparate GB structures. We demonstrate that a microscopic structure metric, the local distortion factor, correlates well with atomically decomposed thermal conductivities obtained from perturbed molecular dynamics for a wide variety of MgO GBs. Based on this correlation, a model for accurately predicting thermal conductivity of GBs is constructed using machine learning techniques. The model reveals that small distortions to local atomic environments are

SUBMITTER: Fujii S 

PROVIDER: S-EPMC7160158 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature