Studies on spin state preferences in Fe(II) complexes

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

Determination of ground-state spins of open-shell transition-metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition-metal complexes. We first identify the limits of distance-based heuristics from distributions of metal–ligand bond lengths of over 2000 unique mononuclear Fe(II)/Fe(III) transition-metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal–ligand bond lengths and classify experimental ground-state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80–90%) of structures. We demonstrate the utility of the ANN by data-mining the literature for spin-crossover (SCO) complexes, which have experimentally observed temperature-dependent geometric structure changes, by correctly assigning almost all (>95%) spin states in the 46 Fe(II) SCO complex set. This approach represents a promising complement to more conventional energy-based spin-state assignment from electronic structure theory at the low cost of a machine learning model.

Density Functional Theory Prediction of the Electrocatalytic Mechanism of Proton Reduction by a Dicobalt Tetrakis(Schiff Base) Macrocycle

A dicobalt tetrakis(Schiff base) macrocycle has recently been reported to electrochemically catalyze the reduction of H⁺ to H₂ in an acetonitrile solution. Density functional theory (DFT) calculations using the ωB97X-D functional are shown to produce structural and thermodynamic results in good agreement with the experimental data. A mechanistic model based on thermodynamics is developed that incorporates electrochemical and magnetic details of the complex, accounting for electron-spin reorganization of the metal center after redox steps. The model is validated through a comparison of the predicted electrochemical potentials with the irreversible cyclic voltammogram of [Co₂LAc]⁺, which shows redox-coupled spin-crossover (RCSCO) behavior for the Co^II/III transitions. Using our model, we predict the thermodynamically favored mechanism of H₂ evolution by [Co₂L]²⁺ to be one of heterolytic proton attack on a [Co^II₂L(μ-H)]⁺ species. Understanding the electronic details and thermodynamically preferred mechanism of this catalyst will aid in improving its efficiency and the future design of bimetallic Co-based H⁺ electrocatalysts. Also, this work will assist in the future DFT modeling of bimetallic RCSCO complexes.

Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics

The origin of distinct 3d vs. 4d transition metal complex sensitivity to exchange is explored over a large data set.