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

Gabriel M. Nascimento1*, Elton Ogoshi1*, Adalberto Fazzio2*, Carlos Mera Acosta1*, Gustavo M. Dalpian1*

1 Center for Natural and Human Sciences, Federal University of ABC, Santo Andre, SP, Brazil

2 Brazilian Nanotechnology National Laboratory (LNNano), CNPEM, 13083‐970, Campinas, São Paulo, Brazil

* Corresponding authors emails: demiranda.gabrielnascimento@gmail.com, elton.ogoshi@gmail.com, adalberto.fazzio@gmail.com, cmeraacosta@gmail.com, dalpian@gmail.com
DOI10.24435/materialscloud:kr-7s [version v1]

Publication date: Dec 17, 2021

How to cite this record

Gabriel M. Nascimento, Elton Ogoshi, Adalberto Fazzio, Carlos Mera Acosta, Gustavo M. Dalpian, High throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds, Materials Cloud Archive 2021.224 (2021), doi: 10.24435/materialscloud:kr-7s.


The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this work, we have built 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 315 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. This repository then contains the main source of data generated in this work, which encompasses full information regarding the materials structure and band structure calculations results, and a database of all the identified spin splittings for these compounds, available in multiple formats.

Materials Cloud sections using this data

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File name Size Description
46.4 MiB Database with the extensive results of this work (material-specific data, band structure description, and spin-splitting description data).
1.1 GiB Materials data, including structure information, band structure calculations results and images.
18.2 KiB Datasets (.csv files) containing general information about the identified spin splittings.
14.1 KiB Detailed description of the data and instructions to access them.


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Preprint (Paper where the workflow and generated data are discussed.)
G. M. Nascimento, E. Ogoshi, A. Fazzio, C. M. Acosta, G. M. Dalpian. High throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds, (2021) (submitted).


2D materials high-throughput spintronics spin splitting Bayesian optimization

Version history:

2021.224 (version v1) [This version] Dec 17, 2021 DOI10.24435/materialscloud:kr-7s