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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Ab lnitio Simulations and Materials Chemistry in the Age of Big Data

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Autor(es):
Schleder, Gabriel Ravanhani [1, 2] ; Padilha, Antonio Claudio M. [2] ; Rocha, Alexandre Reily [3] ; Dalpian, Gustavo Martini [1] ; Fazzio, Adalberto [1, 2]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Fed Univ ABC UFABC, Santo Andre, SP - Brazil
[2] Brazilian Nanotechnol Natl Lab LNNano CNPEM, Campinas, SP - Brazil
[3] Sao Paulo State Univ, Inst Fis Teor, Sao Paulo - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF CHEMICAL INFORMATION AND MODELING; v. 60, n. 2, p. 452-459, FEB 2020.
Citações Web of Science: 1
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: 17/18139-6 - Machine learning e Ciência de Materiais: descoberta e design de materiais 2D
Beneficiário:Gabriel Ravanhani Schleder
Linha de fomento: Bolsas no Brasil - Doutorado
Processo FAPESP: 17/02317-2 - Interfaces em materiais: propriedades eletrônicas, magnéticas, estruturais e de transporte
Beneficiário:Adalberto Fazzio
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 18/05565-0 - Superfícies em semi-metal de Weyl
Beneficiário:Antonio Cláudio Michejevs Padilha
Linha de fomento: Bolsas no Brasil - Pós-Doutorado