  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 to SS type at the valence and/or conduction
bands. The Bayesian optimization captures trends that are used for the
rationalized design of 2D materials with the ideal conditions of band gap and
SS for potential spintronics applications. Our workflow can be applied to any
other material property.
