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

revised on 25.07.2020 and posted on 27.07.2020 by Fang Liu, Chenru Duan, Heather Kulik

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 multi-reference (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 over 4,800 open-shell transition-metal complexes up to hundreds of atoms in size from prior high-throughput DFT studies and evaluate affordable, finite-temperature DFT evaluation of 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 metal- and ligand-sensitivity of the two quantities. We use our trained ML models to rapidly evaluate MR character over a space of ca. 187,000 theoretical complexes, identifying large-scale trends in spin-state-dependent MR character and finding small HOMO–LUMO gap complexes while ensuring low MR character.


Simultaneous mitigation of density and energy errors in approximate DFT for transition metal chemistry

Basic Energy Sciences

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MolSSI fellowship (grant no. ACI-1547580)

ONR N00014-18-1-2434

ONR N00014-20-1-2150

DARPA D18AP00039

Burroughs Wellcome Fund Career Award at the Scientific Interface

AAAS Marion Milligan Mason Award


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Massachusetts Institute of Technology



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Declaration of Conflict of Interest

The authors declare no conflict of interest.