乐胖代购免代理版

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Following our recent work of machine\u2010learning (ML) assisted scheduling optimization [J. Comput. Chem.<\/jats:italic>2023<\/jats:bold>, 44, 1174], we further propose (1) the improved ML models for the better predictions of computational loads, and as such, more elaborate load\u2010balancing 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\u2010normalized exciton model with time\u2010dependent density functional theory (REM\u2010TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. 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