Advanced search
Start date

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

Full text
Nascimento, Gabriel M. ; Ogoshi, Elton ; Fazzio, Adalberto ; Acosta, Carlos Mera ; Dalpian, Gustavo M.
Total Authors: 5
Document type: Journal article
Source: SCIENTIFIC DATA; v. 9, n. 1, p. 18-pg., 2022-04-29.

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. (AU)

FAPESP's process: 17/02317-2 - Interfaces in materials: electronic, magnetic, structural and transport properties
Grantee:Adalberto Fazzio
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 19/04176-2 - Searching for new two dimensional materials: thermodynamic properties
Grantee:Gabriel de Miranda Nascimento
Support Opportunities: Scholarships in Brazil - Scientific Initiation
FAPESP's process: 18/11856-7 - Interface-induced effects in quantum materials
Grantee:Carlos Augusto Mera Acosta
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 18/11641-0 - Machine learning methods applied to the study of interfaces between semiconductors
Grantee:Elton Ogoshi de Melo
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)