Ligand additivity relationships enable efficient exploration of transition metal chemical space
Naveen Arunachalam, Stefan Gugler, Michael G. Taylor, Chenru Duan,, Aditya Nandy, Jon Paul Janet, Ralf Meyer, Jonas Oldenstaedt, Daniel B. K., Chu, and Heather J. Kulik

TL;DR
This paper presents a computational strategy for efficiently exploring transition metal chemical space by leveraging ligand additivity, property interpolation, and targeted property prediction to identify promising Fe(II) complexes.
Contribution
It introduces a ligand additivity-based approach and an improved interpolation scheme for predicting properties, enabling efficient discovery of transition metal complexes.
Findings
Ligand additivity accurately predicts heteroleptic properties from homoleptic complexes.
The property space of feasible Fe(II) complexes is approximately 816,000 compounds.
The multi-stage strategy successfully identified new complexes with targeted properties.
Abstract
To accelerate exploration of chemical space, it is necessary to identify the compounds that will provide the most additional information or value. A large-scale analysis of mononuclear octahedral transition metal complexes deposited in an experimental database confirms an under-representation of lower-symmetry complexes. From a set of around 1000 previously studied Fe(II) complexes, we show that the theoretical space of synthetically accessible complexes formed from the relatively small number of unique ligands is significantly (ca. 816k) larger. For the properties of these complexes, we validate the concept of ligand additivity by inferring heteroleptic properties from a stoichiometric combination of homoleptic complexes. An improved interpolation scheme that incorporates information about cis and trans isomer effects predicts the adiabatic spin-splitting energy to around 2 kcal/mol…
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Taxonomy
TopicsMachine Learning in Materials Science · Computational Drug Discovery Methods · Protein Structure and Dynamics
