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Big data and machine learning for materials science

Rodrigues, Jose F. ; Florea, Larisa ; de Oliveira, Maria C. F. ; Diamond, Dermot ; Oliveira, Osvaldo N.

Discover materials, 2021-12, Vol.1 (1), p.12-12, Article 12 [Periódico revisado por pares]

Cham: Springer International Publishing

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Título:
Big data and machine learning for materials science
Autor: Rodrigues, Jose F. ; Florea, Larisa ; de Oliveira, Maria C. F. ; Diamond, Dermot ; Oliveira, Osvaldo N.
Assuntos: Algorithms ; Artificial intelligence ; Big Data ; Biomaterials ; Characterization and Evaluation of Materials ; Chemical sensors ; Chemistry ; Chemistry and Materials Science ; Computers ; Deep learning ; Energy Materials ; Evolutionary algorithms ; Internet of Things ; Machine learning ; Materials discovery ; Materials Science ; Metallic Materials ; Review ; Structural Materials
É parte de: Discover materials, 2021-12, Vol.1 (1), p.12-12, Article 12
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
Descrição: Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.
Editor: Cham: Springer International Publishing
Idioma: Inglês

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Big data and machine learning for materials science

Rodrigues, Jose F. ; Florea, Larisa ; de Oliveira, Maria C. F. ; Diamond, Dermot ; Oliveira, Osvaldo N.

Cham: Springer International Publishing

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