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

Abstract

AbstractEasy 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.