Ligand Optimization of Exchange Interaction in Co(II) Dimer Single Molecule Magnet by Machine Learning.

Designing single-molecule magnets (SMMs) for potential applications in quantum computing and high-density data storage requires tuning their magnetic properties, especially the strength of the magnetic interaction. These properties can be characterized by first-principles calculations based on density functional theory (DFT). In this work, we study the experimentally synthesized Co(II) dimer (Co 2 (C 5 NH 5 ) 4 (μ-PO 2 (CH 2 C 6 H 5 ) 2 ) 3 ) SMM with the goal to control the exchange energy, Δ E J , between the Co atoms through tuning of the capping ligands. The experimentally synthesized Co(II) dimer molecule has a very small Δ E J < 1 meV. We assemble a DFT data set of 1081 ligand substitutions for the Co(II) dimer. The ligand exchange provides a broad range of exchange energies, Δ E J , from +50 to -200 meV, with 80% of the ligands yielding a small Δ E J < 10 meV. We identify descriptors for the classification and regression of Δ E J using gradient boosting machine learning models. We compare one-hot encoded, structure-based, and chemical descriptors consisting of the HOMO/LUMO energies of the individual ligands and the maximum electronegativity difference and bond order for the ligand atom connecting to Co. We observe a similar overall performance with the chemical descriptors outperforming the other descriptors. We show that the exchange coupling, Δ E J , is correlated to the difference in the average bridging angle between the ferromagnetic and antiferromagnetic states, similar to the Goodenough-Kanamori rules.