InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning

Abstract

We introduce a computational framework (InterMat) to predict band offsets of semiconductor interfaces using density functional theory (DFT) and graph neural networks (GNN). As a first step, we benchmark OptB88vdW...

AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design

“…Moreover, it can be easily extended to other classes of materials such as alloys, semiconductors, themoelectrics, dielectrics, piezoelectrics, solar cells, and so on. Additionally, the application of AtomGPT for defect and interface materials design can also be interesting areas of research in the future.…”