tmQMg* Data Set: Excited State Properties of 74k Transition Metal Complexes

The application of machine learning approaches to meaningful problems in chemistry and materials science is still challenged by the limited availability of data. In order to close this gap, we report the tmQMg* data set, which provides excited state properties for 74k mononuclear transition metal complexes extracted from the Cambridge Structural Database. All properties were computed at the TD-DFT ωB97xd/def2SVP level of theory. The strongest electron excitations in the ultraviolet, visible, and near-infrared ranges are included, together with the wavelengths and intensities of the first 30 excited states. Further, natural transition orbitals were computed for the strongest excitations in the visible range to determine the nature of the associated charge transfers. By computing the TD-DFT spectra in both gas phase and acetone, we quantified solvatochromic effects, which are also provided with the data set, in terms of both wavelength shifts and intensity changes. The tmQMg* data set will enable the development of discriminative and generative artificial intelligence models with respect to absorption spectra, charge transfer character, and solvatochromism, enabling novel advances in the field of transition metal photochemistry.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-10-24
DOI
https://doi.org/10.1021/acs.jcim.5c01958
Primary Topic
Machine Learning in Materials Science
Type
article
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article

tmQMg* Data Set: Excited State Properties of 74k Transition Metal Complexes

David Balcells, Hannes Kneiding
Journal of Chemical Information and Modeling
Machine Learning in Materials Science
article

tmQMg* Data Set: Excited State Properties of 74k Transition Metal Complexes

David Balcells, Hannes Kneiding
article en

Abstract

The application of machine learning approaches to meaningful problems in chemistry and materials science is still challenged by the limited availability of data. In order to close this gap, we report the tmQMg* data set, which provides excited state properties for 74k mononuclear transition metal complexes extracted from the Cambridge Structural Database. All properties were computed at the TD-DFT ωB97xd/def2SVP level of theory. The strongest electron excitations in the ultraviolet, visible, and near-infrared ranges are included, together with the wavelengths and intensities of the first 30 excited states. Further, natural transition orbitals were computed for the strongest excitations in the visible range to determine the nature of the associated charge transfers. By computing the TD-DFT spectra in both gas phase and acetone, we quantified solvatochromic effects, which are also provided with the data set, in terms of both wavelength shifts and intensity changes. The tmQMg* data set will enable the development of discriminative and generative artificial intelligence models with respect to absorption spectra, charge transfer character, and solvatochromism, enabling novel advances in the field of transition metal photochemistry.

Journal of Chemical Information and ModelingVol. 65(21)
University of Oslo (NO)
Openalex Percentile: Top 76%
Machine Learning in Materials Science
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