  A top-level designed forecasting system for predicting computational times of
density-functional theory (DFT)/time-dependent density-functional theory
(TDDFT) calculations is presented. The computational time is assumed as the
intrinsic property for the molecule. Basing on this assumption, the forecasting
system is established using the "reinforced concrete", which combines the
cheminformatics, several machine-learning (ML) models, and the framework of
many-world interpretation (MWI) in multiverse ansatz. Herein, the
cheminformatics is used to recognize the topological structure of molecules,
the ML/AI models are used to build the relationships between topology and
computational cost, and the MWI framework is used to hold various combinations
of DFT functionals and basis sets in DFT/TDDFT calculations. Calculated results
of molecules from DrugBank dataset show that 1) it can give quantitative
predictions of computational costs, typical mean relative errors can be less
than 0.2 for DFT/TDDFT calculations with derivations of 25% using the exactly
pre-trained ML models, 2) it can also be employed to various combinations of
DFT functional and basis set cases without exactly pre-trained ML models, while
only slightly enlarge predicting errors.
