Open AccessDeep learning modeling strategy for material science: from natural materials to metamaterials
Author(s) -
Wenwen Li,
Pu Chen,
Bo Xiong,
Guandong Liu,
Shuliang Dou,
Yaohui Zhan,
Zhiyuan Zhu,
Li Yao,
Wei Ma
Publication year - 2022
Publication title -
jphys materials
Language(s) - English
Resource type - Journals
ISSN - 2515-7639
DOI - 10.1088/2515-7639/ac5914
Subject(s) - computer science , metamaterial , deep learning , transformative learning , flexibility (engineering) , artificial intelligence , relation (database) , big data , data science , realm , data mining , materials science , psychology , pedagogy , statistics , mathematics , optoelectronics , political science , law
Computational modeling is a crucial approach in material-related research for discovering new materials with superior properties. However, the high design flexibility in materials, especially in the realm of metamaterials where the sub-wavelength structure provides an additional degree of freedom in design, poses a formidable computational cost in various real-world applications. With the advent of big data, deep learning (DL) brings revolutionary breakthroughs in many conventional machine learning and pattern recognition tasks such as image classification. The accompanied data-driven modeling paradigm also provides transformative methodology shift in materials science, from trial-and-error routine to intelligent material discovery and analysis. This review systematically summarize the application of DL in material science, based on a model selection perspective for both natural materials and metamaterials. The review aims to uncover the logic behind data-model relation with emphasis on suitable data structures for different scenarios in the material study and the corresponding problem-solving DL model architectures.