Wenwen Li, Pu Chen, Bo Xiong, Guandong Liu, Shuliang Dou, Yaohui Zhan, Zhiyuan Zhu, Tao Chu, Yao Li, Wei Ma
{"title":"Deep learning modeling strategy for material science: from natural materials to metamaterials","authors":"Wenwen Li, Pu Chen, Bo Xiong, Guandong Liu, Shuliang Dou, Yaohui Zhan, Zhiyuan Zhu, Tao Chu, Yao Li, Wei Ma","doi":"10.1088/2515-7639/ac5914","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":16520,"journal":{"name":"Journal of Nonlinear Optical Physics & Materials","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nonlinear Optical Physics & Materials","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/2515-7639/ac5914","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 6
Abstract
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.
期刊介绍:
This journal is devoted to the rapidly advancing research and development in the field of nonlinear interactions of light with matter. Topics of interest include, but are not limited to, nonlinear optical materials, metamaterials and plasmonics, nano-photonic structures, stimulated scatterings, harmonic generations, wave mixing, real time holography, guided waves and solitons, bistabilities, instabilities and nonlinear dynamics, and their applications in laser and coherent lightwave amplification, guiding, switching, modulation, communication and information processing. Original papers, comprehensive reviews and rapid communications reporting original theories and observations are sought for in these and related areas. This journal will also publish proceedings of important international meetings and workshops. It is intended for graduate students, scientists and researchers in academic, industrial and government research institutions.