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|a 10.1002/jcc.27459
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|a eng
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|a Yuan, Kai
|e verfasserin
|4 aut
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|a Fault-tolerant quantum chemical calculations with improved machine-learning models
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a Date Revised 10.10.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2024 Wiley Periodicals LLC.
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|a 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
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|a Journal Article
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|a coded computing
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|a exciton model
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|a fragmented approach
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|a interacting energy
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| 650 |
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|a load‐balancing
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|a Zhou, Shuai
|e verfasserin
|4 aut
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| 700 |
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|a Li, Ning
|e verfasserin
|4 aut
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| 700 |
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|a Li, Tianyan
|e verfasserin
|4 aut
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| 700 |
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|a Ding, Bowen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Danhuai
|e verfasserin
|4 aut
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| 700 |
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|a Ma, Yingjin
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 45(2024), 31 vom: 05. Dez., Seite 2640-2658
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnas
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| 773 |
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|g volume:45
|g year:2024
|g number:31
|g day:05
|g month:12
|g pages:2640-2658
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|u http://dx.doi.org/10.1002/jcc.27459
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