High-throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds.

ABSTRACT: The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 358 2D materials according t

SUBMITTER: Nascimento GM 

PROVIDER: S-EPMC9054849 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature