Leverage electron properties to predict phonon properties via transfer learning for semiconductors.

ABSTRACT: Electron properties are usually easier to obtain than phonon properties. The ability to leverage electron properties to help predict phonon properties can thus greatly benefit materials by design for applications like thermoelectrics and electronics. Here, we demonstrate the ability of using transfer learning (TL), where knowledge learned from training machine learning models on electronic bandgaps of 1245 semiconductors is transferred to improve the models, trained using only 124 data, for predicting various phonon properties (phonon bandgap, group velocity, and heat capacity). Compared to directly trained models, TL reduces the mean absolute errors of prediction by 65, 14, and 54% respectively, for the three phonon properties. The TL models are further validated using several semiconduct

SUBMITTER: Liu Z 

PROVIDER: S-EPMC7673693 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature