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BOS-TMC Data Set: DFT Properties of 159k Experimentally Characterized Transition Metal Complexes Spanning Multiple
Aaron G Garrison1, Jacob W Toney1, Tatiana Nikolaeva1,2,3
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States.
Abstract:
We present the Boston Open-Shell Transition Metal Complex (BOS-TMC) data set, a set of density functional theory (DFT) properties for 159k experimentally characterized mononuclear transition metal complexes (TMCs) in multiple spin states with a range of formal charges derived from the Cambridge Structural Database (CSD). To curate this set, we carried out an iterative procedure to confidently assign the overall TMC charge. From this information, we then obtained properties in up to three spin states, i.e., low-, intermediate-, and high-spin for 3d metals and low- and intermediate-spin for 4d and 5d metals, depending on compatibility with the metal electron configuration, for a total of 343.8k TMC/spin combinations. At odds with prior sets, we preserved experimental heavy atom coordinates in these structures during optimization. We report all properties using PBE0/def2-TZVP single-point energies for these structures. We introduce a scheme for computing metal-spin-dependent atomization energies, which we report for each TMC. Alongside electronic energies, we report up to seven additional properties, including HOMO and LUMO levels, HOMO-LUMO gap, atomic partial charges, dipole moments, atomization energies, and spin-splitting energies for a total of over 2.9 M TMC-associated properties. For a representative subset of over 10k complexes chosen based on size, we evaluate the sensitivity of computed properties to exchange-correlation (xc) functional choice from a set of 12 xc functionals spanning multiple rungs of "Jacob's ladder," highlighting hotspots of TMC space that have the greatest uncertainty with respect to xc choice. In comparison to prior experimentally derived TMC data sets, BOS-TMC is both larger and more diverse in terms of charge and spin configurations and, as a result, more diverse in its range of properties. This data set is expected to provide a high-fidelity foundation for machine learning model development, DFT benchmarking, and exploration of transition metal chemistry.
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