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tmQMg* Data Set: Excited State Properties of 74k Transition Metal Complexes.
Hannes Kneiding1, David Balcells1
1Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway.
Journal of Chemical Information and Modeling
|October 24, 2025
Summary
A new dataset of 74,000 transition metal complexes, tmQMg*, offers excited state properties to address data scarcity in machine learning for chemistry. This resource advances artificial intelligence models for transition metal photochemistry.
Area of Science:
- Computational Chemistry
- Materials Science
- Photochemistry
Background:
- Machine learning applications in chemistry and materials science are hindered by limited data availability.
- Developing predictive models requires comprehensive datasets of chemical and physical properties.
Purpose of the Study:
- To introduce the tmQMg* dataset, containing excited state properties for 74,000 mononuclear transition metal complexes.
- To facilitate the development of artificial intelligence models for predicting absorption spectra, charge transfer, and solvatochromism.
Main Methods:
- Computed excited state properties using time-dependent density functional theory (TD-DFT) at the ωB97xd/def2SVP level.
- Extracted data from the Cambridge Structural Database.
- Calculated wavelengths, intensities, and natural transition orbitals for electron excitations.
- Quantified solvatochromic effects in gas phase and acetone.
Main Results:
- The tmQMg* dataset includes excited state properties for 74,000 transition metal complexes.
- Data encompasses UV, visible, and near-infrared excitations, including wavelengths, intensities, and charge transfer characteristics.
- Solvatochromic effects were quantified, providing insights into solvent-dependent spectral changes.
Conclusions:
- The tmQMg* dataset addresses the critical need for data in machine learning for chemistry.
- This resource will enable advancements in AI-driven discovery for transition metal photochemistry.
- Facilitates the creation of more accurate and versatile computational models.
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