This record contains data structures used in the manuscript titled 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 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 between the Co atoms through tuning of the capping ligands. The experimentally synthesized Co(II) dimer molecule has a very small exchange energy (< 1meV). We assemble a DFT dataset of 1081 ligand-substitutions for the Co(II) dimer. The ligand exchange provides a broad range of exchange energies from +50 meV to -200 meV, with 80% of the ligands yielding a small exchange energies (<10 meV). We identify descriptors for the classification and regression of exchange energies 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 outperforming the other descriptors. The record contains: 1) get_data.py: the code and descriptions for loading the data and structures 2) structures_xyz: a folder containing structure files in .xyz format 3) Co_dimer_data.csv and Co_dimer_all.csv: the data files in .csv format