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Advances of machine learning in materials science: Ideas and techniques

Chong, Sue Sin ; Ng, Yi Sheng ; Wang, Hui-Qiong ; Zheng, Jin-Cheng

Frontiers of physics, 2024-02, Vol.19 (1), p.13501, Article 13501 [Periódico revisado por pares]

Beijing: Higher Education Press

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  • Título:
    Advances of machine learning in materials science: Ideas and techniques
  • Autor: Chong, Sue Sin ; Ng, Yi Sheng ; Wang, Hui-Qiong ; Zheng, Jin-Cheng
  • Assuntos: Astronomy ; Astrophysics and Cosmology ; Atomic ; Big Data ; Condensed Matter Physics ; Machine learning ; Materials science ; Molecular ; Optical and Plasma Physics ; Particle and Nuclear Physics ; Physics ; Physics and Astronomy ; Review Article
  • É parte de: Frontiers of physics, 2024-02, Vol.19 (1), p.13501, Article 13501
  • Notas: Document received on :2022-07-27
    Document accepted on :2023-06-18
    machine learning
    materials science
  • Descrição: In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties; ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software; The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.
  • Editor: Beijing: Higher Education Press
  • Idioma: Inglês
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