tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes
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Abstract
We report the transition metal quantum mechanics (tmQM) data set, which contains the geometries and properties of a large transition metal–organic compound space. tmQM comprises 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic, and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d, and 5d from groups 3 to 12). All complexes are closed-shell, with a formal charge in the range {+1, 0, −1}e. The tmQM data set provides the Cartesian coordinates of all metal complexes optimized at the GFN2-xTB level, and their molecular size, stoichiometry, and metal node degree. The quantum properties were computed at the DFT(TPSSh-D3BJ/def2-SVP) level and include the electronic and dispersion energies, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, HOMO/LUMO gap, dipole moment, and natural charge of the metal center; GFN2-xTB polarizabilities are also provided. Pairwise representations showed the low correlation between these properties, providing nearly continuous maps with unusual regions of the chemical space, for example, complexes combining large polarizabilities with wide HOMO/LUMO gaps and complexes combining low-energy HOMO orbitals with electron-rich metal centers. The tmQM data set can be exploited in the data-driven discovery of new metal complexes, including predictive models based on machine learning. These models may have a strong impact on the fields in which transition metal chemistry plays a key role, for example, catalysis, organic synthesis, and materials science. tmQM is an open data set that can be downloaded free of charge from https://github.com/bbskjelstad/tmqm.
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The tmQM data set contains 86,000 structures of DFT and low-cost, semiempirical properties.
“the 86,000 structures in the tmQM data set of DFT and low-cost, semiempirical properties (31)”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [31]
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- Authors
- Balcells, D., & Skjelstad, B. B.
- Affiliations
- University of Oslo (NO)
- Hokkaido University (JP)
- Corresponding author
- Balcells, David
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-11-09
- Volume
- 60
- Issue
- 12
- Pages
- 6135-6146
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC7768608
- OpenAlex
- W3098543521
- Cited by
- 143
- References
- 97
- Citations by year
- 2026: 35 · 2025: 34 · 2024: 27 · 2023: 12 · 2022: 15 · 2021: 16 · 2020: 4
- FWCI
- 4.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Royal College of Surgeons in Ireland (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Funders
- Norges Forskningsr?d (262695)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- transition metal complexes, mononuclear complexes, quantum chemical properties, molecular geometry, HOMO-LUMO gap, polarizability, electronic energy, dispersion energy, chemical space, machine learning property prediction, Cambridge Structural Database, organometallic compounds
- MeSH terms
- Electrons, Ligands, Metals, Models, Molecular, Coordination Complexes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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