Fault-tolerant quantum chemical calculations with improved machine-learning models

doi:10.1002/jcc.27459

	Fault-tolerant quantum chemical calculations with improved machine-learning models
	Yuan, Kai 1,2; Zhou, Shuai 3,4; Li, Ning 5; Li, Tianyan 2; Ding, Bowen 6; Guo, Danhuai 1; Ma, Yingjin 2
	2024-07-29
发表期刊	JOURNAL OF COMPUTATIONAL CHEMISTRY
ISSN	0192-8651
页码	19
摘要	Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine-learning (ML) assisted scheduling optimization [J. Comput. Chem. 2023, 44, 1174], we further propose (1) the improved ML models for the better predictions of computational loads, and as such, more elaborate load-balancing calculations can be expected; (2) the idea of coded computation, that is, the integration of gradient coding, in order to introduce fault tolerance during the distributed calculations; and (3) their applications together with re-normalized exciton model with time-dependent density functional theory (REM-TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. The results show that the improved ML-assisted coded calculations can further improve the load-balancing and cluster utilization, owing primarily profit in fault tolerance that aims at the automated quantum chemical calculations for both ground and excited states. We present a procedure for easy and effective implementations of coded quantum chemical calculations with improved machine-learning (ML) models. Employing this procedure, we show that the improved ML-assisted coded calculations can further improve the load-balancing and cluster utilization, owing primarily profit in fault tolerance that aims at the automated quantum chemical calculations for both ground and excited states. image
关键词	coded computing exciton model fragmented approach interacting energy load-balancing
DOI	10.1002/jcc.27459
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[22173114] ; National Natural Science Foundation of China[22333003] ; National Natural Science Foundation of China[42371476] ; Chinese Academy of Sciences[XDB0500101] ; Chinese Academy of Sciences[YIPA2022168] ; Chinese Academy of Sciences[CAS-WX2021SF-0103-02] ; Chinese Academy of Sciences[CNIC20230201] ; Fundamental Research Funds for the Central Universities[BUCTRC202132]
WOS研究方向	Chemistry
WOS类目	Chemistry, Multidisciplinary
WOS记录号	WOS:001278715700001
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39684
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ding, Bowen; Guo, Danhuai; Ma, Yingjin
作者单位	1.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China 2.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China 5.Wenzhou Univ, Coll Chem & Mat Engn, Wenzhou, Peoples R China 6.Chinese Acad Sci, Inst Chem, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yuan, Kai,Zhou, Shuai,Li, Ning,et al. Fault-tolerant quantum chemical calculations with improved machine-learning models[J]. JOURNAL OF COMPUTATIONAL CHEMISTRY,2024:19.
APA	Yuan, Kai.,Zhou, Shuai.,Li, Ning.,Li, Tianyan.,Ding, Bowen.,...&Ma, Yingjin.(2024).Fault-tolerant quantum chemical calculations with improved machine-learning models.JOURNAL OF COMPUTATIONAL CHEMISTRY,19.
MLA	Yuan, Kai,et al."Fault-tolerant quantum chemical calculations with improved machine-learning models".JOURNAL OF COMPUTATIONAL CHEMISTRY (2024):19.