Daniel King

TL;DR: An automated machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules shows that a "black-box" mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.

TL;DR: Automatic state interaction with large localized active spaces for multimetallic systems efficiently converges to complete active space configuration interaction in the limit of large model spaces.

TL;DR: Researchers introduce "discrete variational selection" (DVS) for active space selection, using multiconfiguration pair-density functional theory (MC-PDFT) to select optimal active spaces with a mean unsigned error of 0.17 eV for 207 vertical excitations in small-to-medium-sized molecules.

TL;DR: This study evaluates density functionals for describing small energy differences in metal-organic framework (MOF) reactivity, finding significant variance and disagreement between functionals, highlighting challenges in accurately predicting MOF catalytic trends.