Details
| Original language | English |
|---|---|
| Pages (from-to) | 439-460 |
| Number of pages | 22 |
| Journal | Nanophotonics |
| Volume | 11 |
| Issue number | 3 |
| Publication status | Published - 4 Jan 2022 |
Abstract
Keywords
- 2D materials, hierarchical structure, inverse design, machine learning, metamaterials, phononic crystals, DEEP NEURAL-NETWORKS, ARTIFICIAL-INTELLIGENCE, THERMAL-CONDUCTIVITY, INVERSE DESIGN, OPTIMIZATION, TRANSPORT, 1ST-PRINCIPLES, ORGANIZATION, PHOTONICS, CRYSTALS
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Electrical and Electronic Engineering
- Biochemistry, Genetics and Molecular Biology(all)
- Biotechnology
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In: Nanophotonics, Vol. 11, No. 3, 04.01.2022, p. 439-460.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Intelligent on-demand design of phononic metamaterials
AU - Jin, Yabin
AU - He, Liangshu
AU - Wen, Zhihui
AU - Mortazavi, B
AU - Guo, HW
AU - Torrent, D
AU - Djafari-Rouhani, B
AU - Rabczuk, T
AU - Zhuang, XY
AU - Li, Yan
N1 - Funding Information: Research funding: This work is supported by the National Key R&D Program of China (Grant Nos. 2020YFA0211402), the National Natural Science Foundation of China (11902223), the program for professor of special appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, the High-Level Foreign Expert Program, the Fundamental Research Funds for the Central Universities. D.T. acknowledges financial support through the “Ramon y Cajal” fellowship, under Grant No. RYC-2016-21188, from the Ministry of Science, Innovation, and Universities, through Project No. RTI2018-093921-AC42 and from the Universitat Jaume I through Project No. UJI-A2018-08.
PY - 2022/1/4
Y1 - 2022/1/4
N2 - 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.
AB - 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.
KW - 2D materials
KW - hierarchical structure
KW - inverse design
KW - machine learning
KW - metamaterials
KW - phononic crystals
KW - DEEP NEURAL-NETWORKS
KW - ARTIFICIAL-INTELLIGENCE
KW - THERMAL-CONDUCTIVITY
KW - INVERSE DESIGN
KW - OPTIMIZATION
KW - TRANSPORT
KW - 1ST-PRINCIPLES
KW - ORGANIZATION
KW - PHOTONICS
KW - CRYSTALS
UR - http://www.scopus.com/inward/record.url?scp=85122626524&partnerID=8YFLogxK
U2 - 10.1515/nanoph-2021-0639
DO - 10.1515/nanoph-2021-0639
M3 - Article
VL - 11
SP - 439
EP - 460
JO - Nanophotonics
JF - Nanophotonics
SN - 2192-8606
IS - 3
ER -