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

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal do ABC (UFABC)-
Autor(es): dc.contributorBrazilian Nanotechnol Natl Lab LNNano CNPEM-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorSchleder, Gabriel Ravanhani-
Autor(es): dc.creatorPadilha, Antonio Claudio M.-
Autor(es): dc.creatorRocha, Alexandre Reily [UNESP]-
Autor(es): dc.creatorDalpian, Gustavo Martini-
Autor(es): dc.creatorFazzio, Adalberto-
Data de aceite: dc.date.accessioned2022-02-22T00:09:59Z-
Data de disponibilização: dc.date.available2022-02-22T00:09:59Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1021/acs.jcim.9b00781-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/196627-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/196627-
Descrição: dc.descriptionIn 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.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFed Univ ABC UFABC, Santo Andre, SP, Brazil-
Descrição: dc.descriptionBrazilian Nanotechnol Natl Lab LNNano CNPEM, Campinas, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Inst Fis Teor, Sao Paulo, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Inst Fis Teor, Sao Paulo, Brazil-
Descrição: dc.descriptionFAPESP: 2017/18139-6-
Descrição: dc.descriptionFAPESP: 18/05565-0-
Descrição: dc.descriptionFAPESP: 17/02317-2-
Formato: dc.format452-459-
Idioma: dc.languageen-
Publicador: dc.publisherAmer Chemical Soc-
Relação: dc.relationJournal Of Chemical Information And Modeling-
???dc.source???: dc.sourceWeb of Science-
Título: dc.titleAb lnitio Simulations and Materials Chemistry in the Age of Big Data-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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