Understanding, discovery, and synthesis of 2D materials enabled by machine learning.
Chem Soc Rev
; 51(6): 1899-1925, 2022 Mar 21.
Article
em En
| MEDLINE
| ID: mdl-35246673
ABSTRACT
Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. We introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Eletrônica
/
Aprendizado de Máquina
Idioma:
En
Revista:
Chem Soc Rev
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos