Intelligent on-demand design of phononic metamaterials

authored by
Yabin Jin, Liangshu He, Zhihui Wen, B Mortazavi, HW Guo, D Torrent, B Djafari-Rouhani, T Rabczuk, XY Zhuang, Yan Li
Abstract

With the growing interest in the field of artificial materials, more advanced and sophisticated functionalities are required from phononic crystals and acoustic metamaterials. This implies a high computational effort and cost, and still the efficiency of the designs may be not sufficient. With the help of third-wave artificial intelligence technologies, the design schemes of these materials are undergoing a new revolution. As an important branch of artificial intelligence, machine learning paves the way to new technological innovations by stimulating the exploration of structural design. Machine learning provides a powerful means of achieving an efficient and accurate design process by exploring nonlinear physical patterns in high-dimensional space, based on data sets of candidate structures. Many advanced machine learning algorithms, such as deep neural networks, unsupervised manifold clustering, reinforcement learning and so forth, have been widely and deeply investigated for structural design. In this review, we summarize the recent works on the combination of phononic metamaterials and machine learning. We provide an overview of machine learning on structural design. Then discuss machine learning driven on-demand design of phononic metamaterials for acoustic and elastic waves functions, topological phases and atomic-scale phonon properties. Finally, we summarize the current state of the art and provide a prospective of the future development directions.

Organisation(s)
PhoenixD: Photonics, Optics, and Engineering - Innovation Across Disciplines
Institute of Photonics
External Organisation(s)
Universitat Jaume I
CHRU de Lille
Bauhaus-Universität Weimar
Tongji University
Type
Article
Journal
Nanophotonics
Volume
11
Pages
439-460
No. of pages
22
ISSN
2192-8606
Publication date
04.01.2022
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Electronic, Optical and Magnetic Materials, Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering, Biotechnology
Electronic version(s)
https://doi.org/10.1515/nanoph-2021-0639 (Access: Open)