中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Fault-tolerant quantum chemical calculations with improved machine-learning models

文献类型：期刊论文

作者Yuan, Kai1,2; Zhou, Shuai3,4; Li, Ning5; Li, Tianyan2; Ding, Bowen6; Guo, Danhuai1; Ma, Yingjin2
刊名JOURNAL OF COMPUTATIONAL CHEMISTRY
出版日期2024-07-29
页码19
关键词coded computing exciton model fragmented approach interacting energy load-balancing
ISSN号0192-8651
DOI10.1002/jcc.27459
英文摘要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
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
语种英语
WOS记录号WOS:001278715700001
出版者WILEY
源URL[http://119.78.100.204/handle/2XEOYT63/39683]  
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。