skip to main content
Idioma:

Materials Informatics for 2D Materials Combined with Sparse Modeling and Chemical Perspective: Toward Small-Data-Driven Chemistry and Materials Science

Oaki, Yuya ; Igarashi, Yasuhiko

Bulletin of the Chemical Society of Japan, 2021-10, Vol.94 (10), p.2410-2422 [Periódico revisado por pares]

Tokyo: The Chemical Society of Japan

Texto completo disponível

Citações Citado por
  • Título:
    Materials Informatics for 2D Materials Combined with Sparse Modeling and Chemical Perspective: Toward Small-Data-Driven Chemistry and Materials Science
  • Autor: Oaki, Yuya ; Igarashi, Yasuhiko
  • Assuntos: Bioinformatics ; Design optimization ; Informatics ; Machine learning ; Masterpiece Materials with Functional Excellence ; Materials information ; Materials science ; Model accuracy ; Modelling ; Prediction models ; Two dimensional materials ; Two dimensional models
  • É parte de: Bulletin of the Chemical Society of Japan, 2021-10, Vol.94 (10), p.2410-2422
  • Descrição: Application of data-scientific approaches to conventional sciences, such as chemo-informatics, bio-informatics, and materials informatics (MI), has attracted much interest toward data-driven research. The concept enables accelerated discovery of new materials, enhancement of performance, and optimization of processes. However, sufficient bigdata is not always prepared to apply to machine learning. For example, experimental scientists have their own small data including success and failure in their laboratory, whether in academia or industry. If such small data is effectively utilized with a data-scientific approach, research activities can be accelerated without energy, resource, and cost consumption. This account focuses on MI for small data, a recent concept for application of small data, with introduction of model cases, such as control of exfoliation processes to obtain 2D materials. Combination of machine learning and chemical perspective is effective for construction of straightforward and interpretable predictors through the extraction of a limited number of descriptors from small dataset. Although the prediction accuracy is not so precise, the model has enough accuracy to be a guideline reducing the number of the next experiments. The present MI for small data opens potentials of small-data-driven chemistry and materials science.
  • Editor: Tokyo: The Chemical Society of Japan
  • Idioma: Inglês
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.