南方科技大学知识苑(SUSTech KC): A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networks

题名	A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networks
作者	Zhu, Changliang1,2,3 ; Fang, Chenchao 1,2,4; Jin, Zhipeng 1,2; Li, Baowen3,5,6,7 ; Shen, Xiangying3 ; Xu, Lei 1,2
通讯作者	Shen, Xiangying; Xu, Lei
发表日期	2024-09-01
DOI	10.1063/5.0199530
发表期刊	APPLIED PHYSICS REVIEWS 影响因子和分区
ISSN	1931-9401
卷号	11 期号:3
摘要	"AI for science" is widely recognized as a future trend in the development of scientific research. Currently, although machine learning algorithms have played a crucial role in scientific research with numerous successful cases, relatively few instances exist where AI assists researchers in uncovering the underlying physical mechanisms behind a certain phenomenon and subsequently using that mechanism to improve machine learning algorithms' efficiency. This article uses the investigation into the relationship between extreme Poisson's ratio values and the structure of amorphous networks as a case study to illustrate how machine learning methods can assist in revealing underlying physical mechanisms. Upon recognizing that the Poisson's ratio relies on the low-frequency vibrational modes of the dynamical matrix, we can then employ a convolutional neural network, trained on the dynamical matrix instead of traditional image recognition, to predict the Poisson's ratio of amorphous networks with a much higher efficiency. Through this example, we aim to showcase the role that artificial intelligence can play in revealing fundamental physical mechanisms, which subsequently improves the machine learning algorithms significantly.
相关链接	[来源记录]
收录类别	SCI ; EI
语种	英语
学校署名	通讯
资助项目	NSFC[12205138] ; Shenzhen Science and Technology Innovation Committee (SZSTI)[JCYJ20220530113206015] ; null[NSFC-12074325] ; null[GRF-14307721] ; null[GRF-14306923] ; null[CRF-C6016-20G] ; null[CRF-C1018-17G] ; null[4053582] ; null[2024A1515030139]
WOS研究方向	Physics
WOS类目	Physics, Applied
WOS记录号	WOS:001262277700001
出版者	AIP Publishing
EI入藏号	20242816671790
EI主题词	Image recognition ; Learning algorithms ; Machine learning ; Poisson ratio
EI分类号	Artificial Intelligence:723.4 ; Machine Learning:723.4.2 ; Production Engineering:913.1 ; Materials Science:951
来源库	Web of Science
引用统计
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/786591
专题	理学院_物理系工学院_材料科学与工程系工学院_深港微电子学院
作者单位	1.Chinese Univ Hong Kong, Dept Phys, Hong Kong, Peoples R China 2.Chinese Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Peoples R China 3.Southern Univ Sci & Technol, Dept Phys, Shenzhen 518055, Peoples R China 4.Univ Elect Sci & Technol China, Shenzhen Inst Adv Study, Shenzhen 518110, Peoples R China 5.Southern Univ Sci & Technol, Dept Mat Sci & Engn, Shenzhen 518055, Peoples R China 6.Southern Univ Sci & Technol, Sch Microelect, Shenzhen 518055, Peoples R China 7.Shenzhen Int Quantum Acad, Shenzhen 518048, Peoples R China
第一作者单位	物理系
通讯作者单位	物理系
推荐引用方式 GB/T 7714	Zhu, Changliang,Fang, Chenchao,Jin, Zhipeng,et al. A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networks[J]. APPLIED PHYSICS REVIEWS,2024,11(3).
APA	Zhu, Changliang,Fang, Chenchao,Jin, Zhipeng,Li, Baowen,Shen, Xiangying,&Xu, Lei.(2024).A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networks.APPLIED PHYSICS REVIEWS,11(3).
MLA	Zhu, Changliang,et al."A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networks".APPLIED PHYSICS REVIEWS 11.3(2024).