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, es-pecially the strength of the magnetic interaction. These properties can be characterized by first-principles calculation based on density functional theory (DFT). In this work, we study the experimentally synthesized Co(II) dimer SMM with the goal to control the exchange energy, ΔE_J, between the Co atoms through tuning of the capping ligands. The experimentally synthesized Co(II) dimer molecule has avery small ΔE_J<1 meV. We assemble a DFT dataset of 1081 ligand-substitutions for the Co(II) dimer. The ligand exchange provides abroad range of exchange energies, ΔE_J, from+50 meV to -200 meV, with 80% of the ligands yielding a small ΔE_J<10 meV. We identify descriptors for the classification and regression of ΔE_J using gradient boosting machine learning models. We compare structure-based, one-hot encoded, 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 out-performing the other descriptors. We show that the exchange coupling, ΔE_J, is correlated to the difference in the average bridging angle between the ferromagnetic and antiferromagnetic states, similar to the Goodenough–Kanamori rules.