16TMCONF543: An Automatically Generated Data Set of Conformational Energies of Transition Metal Complexes Relevant to Catalysis
A database of conformational energies (CEs) of 16 transition metal (TM) complexes relevant to catalysis is automatically created by employing the new conformer generator program Uniconf. The generation procedure starts from an arbitrary high-energy structure and consistently produces conformer(s) similar to or even more energetically favorable than the optimized reference conformer retrieved from the Cambridge Structural Database. The reference CEs obtained with common dispersion-corrected functionals are employed to test the low-cost semiempirical methods. The superiority of the GFNn-xTB schemes over PM6*/7 methods is confirmed by the basic statistical analysis of the CEs. In addition, the influence of the vibrational thermostatistical (ΔGtherm) and continuum solvation (ΔGsolv) corrections on the CEs is examined in the framework of the (modified) scaled rigid-rotor-harmonic oscillator, (m)sRRHO, approximation, and solvation model based on density (SMD). In general, conformational Gibbs free energies exhibit excellent correlation with the respective electronic energies, especially if a more robust msRRHO scheme is employed for the calculation of ΔGtherm and in the case of a nonpolar solvent. The deviations from the perfect correlation occur if reduced CE windows of <5 kcal mol–1 are considered, implying a greater influence of these effects on the sorting of the low-energy structures.
Authors
- Arseniy A. Otlyotov (ORCID: https://orcid.org/0000-0001-6974-6327)
- Andrey D. Moshchenkov (ORCID: https://orcid.org/0009-0008-5503-9873)
- Yury V. Minenkov (ORCID: https://orcid.org/0000-0001-8993-056X)
- Timofey P. Rozov
Institutions
- Lomonosov Moscow State University (RU)
- Semenov Institute of Chemical Physics (RU)
Publication Details
- Journal
- Organometallics
- Published
- 2024-09-17
- DOI
- https://doi.org/10.1021/acs.organomet.4c00246
- Citations
- 3
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- Russian Science Foundation