Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening

Despite its widespread use in chemical
discovery, approximate density
functional theory (DFT) is poorly suited to many targets, such as
those containing open-shell, 3d transition metals that can be expected
to have strong multireference (MR) character. For discovery workflows
to be predictive, we need automated, low-cost methods that can distinguish
the regions of chemical space where DFT should be applied from those
where it should not. We curate more than 4800 open-shell transition-metal
complexes up to hundreds of atoms in size from prior high-throughput
DFT studies and evaluate affordable, finite-temperature DFT fractional
occupation number (FON)-based MR diagnostics. We show that intuitive
measures of strong correlation (i.e., the HOMO–LUMO gap) are
not predictive of MR character as judged by FON-based diagnostics.
Analysis of independently trained machine learning (ML) models to
predict HOMO–LUMO gaps and FON-based diagnostics reveals differences
in the metal and ligand sensitivity of the two quantities. We use
our trained ML models to rapidly evaluate MR character over a space
of ∼187000 theoretical complexes, identifying large-scale trends
in spin-state-dependent MR character and finding small HOMO–LUMO
gap complexes while ensuring low MR character.