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

Autor(es)

Nascimento, Gabriel M. ; Ogoshi, Elton ; Fazzio, Adalberto ; Acosta, Carlos Mera ; Dalpian, Gustavo M.

• Tipo de documento: Artigo Científico
• Fonte: SCIENTIFIC DATA; v. 9, n. 1, p. 18-pg., 2022-04-29.

Resumo

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)

• Processo FAPESP: 17/02317-2 - Interfaces em materiais: propriedades eletrônicas, magnéticas, estruturais e de transporte
• Beneficiário: Adalberto Fazzio
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 19/04176-2 - Na busca de novos materiais bidimensionais: propriedades termodinâmicas
• Beneficiário: Gabriel de Miranda Nascimento
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 18/11856-7 - Efeitos induzidos por interfaces em materiais quânticos
• Beneficiário: Carlos Augusto Mera Acosta
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 18/11641-0 - Métodos de machine learning aplicados a interfaces entre materiais semicondutores
• Beneficiário: Elton Ogoshi de Melo
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto