| Texto completo | |
| Autor(es): |
Schleder, Gabriel Ravanhani
;
Padilha, Antonio Claudio M.
;
Rocha, Alexandre Reily
;
Dalpian, Gustavo Martini
;
Fazzio, Adalberto
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | JOURNAL OF CHEMICAL INFORMATION AND MODELING; v. 60, n. 2, p. 8-pg., 2020-02-01. |
| Resumo | |
In this perspective, we discuss computational advances in the last decades, both in algorithms as well as in technologies, that enabled the development, widespread use, and maturity of simulation methods for molecular and materials systems. Such advances led to the generation of large amounts of data, which required the creation of several computational databases. Within this scenario, with the democratization of data access, the field now encounters several opportunities for data-driven approaches toward chemical and materials problems. Specifically, machine learning methods for predictions of novel materials or properties are being increasingly used with great success. However, black box usage fails in many instances; several technical details require expert knowledge in order for the predictions to be useful, such as with descriptors and algorithm selection. These approaches represent a direction for further developments, notably allowing advances for both developed and emerging countries with modest computational infrastructures. (AU) | |
| Processo FAPESP: | 18/05565-0 - Superfícies em semi-metal de Weyl |
| Beneficiário: | Antonio Cláudio Michejevs Padilha |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 17/18139-6 - Machine learning e Ciência de Materiais: descoberta e design de materiais 2D |
| Beneficiário: | Gabriel Ravanhani Schleder |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |
| 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 |