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The development of spintronic devices demands the existence of materials withsome kind of spin splitting (SS). In this Data Descriptor, we build a databaseof ab initio calculated SS in 2D materials. More than that, we propose aworkflow for materials design integrating an inverse design approach and aBayesian inference optimization. We use the prediction of SS prototypes forspintronic 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 filtersfor materials screening, and followed by density functional theory (DFT)calculations. Applying this process to the C2DB database, we identify andclassify 358 2D materials according to SS type at the valence and/or conductionbands. The Bayesian optimization captures trends that are used for therationalized design of 2D materials with the ideal conditions of band gap andSS for potential spintronics applications. Our workflow can be applied to anyother material property.

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