Fault-tolerant quantum chemical calculations with improved machine-learning models
© 2024 Wiley Periodicals LLC.
| Publié dans: | Journal of computational chemistry. - 1984. - 45(2024), 31 vom: 05. Dez., Seite 2640-2658 |
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| Auteur principal: | |
| Autres auteurs: | , , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2024
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| Accès à la collection: | Journal of computational chemistry |
| Sujets: | Journal Article coded computing exciton model fragmented approach interacting energy load‐balancing |
| Résumé: | © 2024 Wiley Periodicals LLC. 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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| Description: | Date Revised 10.10.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.27459 |