Band Alignment of Oxides by Learnable Structural-Descriptor-Aided Neural Network and Transfer Learning.

ABSTRACT: The band alignment of semiconductors, insulators, and dielectrics is relevant to diverse material properties and device structures utilizing their surfaces and interfaces. In particular, the ionization potential and electron affinity are fundamental quantities that describe surface-dependent band-edge positions with respect to the vacuum level. Their accurate and systematic determination, however, demands elaborate experiments or simulations for well-characterized surfaces. Here, we report machine learning for the band alignment of nonmetallic oxides using a high-throughput first-principles calculation data set containing about 3000 oxide surfaces. Our neural network accurately predicts the band positions for relaxed surfaces of binary oxides simply by using the information on bulk structu

SUBMITTER: Kiyohara S 

PROVIDER: S-EPMC11009958 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature