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 -