Building the Data Foundations for the Design and Discovery of Transition Metal Complexes
7 affiliations
- 1Physical and Theoretical Chemistry Laboratory, Department of Chemistry
- 2University of Oxford
- 3Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Oslo, Norway, 0315
- 4Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, Mülheim an der Ruhr, Germany, 45470
- 5Department of Mathematics, University of Oslo, Blindern, Oslo, Norway, 0371
- 6Centre for Materials Science and Nanotechnology, Department of Chemistry
- 7University of Oslo
Abstract
The unique chemistry of transition metal complexes (TMCs) enables several advanced technologies in diverse fields including renewable energies, medicine, and catalysis. Machine learning methods can accelerate their rational design but are bounded by limited dataset sizes, restricted chemical scopes, and insufficient quality. This Perspective surveys the current landscape of TMC datasets and investigates how they seek to address these challenges. The analysis focuses on the data curation processes as well as the scope of the properties provided, and their level of theory. Furthermore, the applications and methodological advancements enabled by TMC datasets are reviewed, including the generative design of metal ligands and the evolutionary optimization of TMCs. The Perspective concludes by outlining future directions, with particular emphasis on extending data to broader chemical scopes including non-equilibrium geometries, establishing community-wide data formats and benchmarks, and developing strategies to effectively integrate heterogeneous data sources.
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BibTeX
@misc{kneiding2026building,
title = {{Building the Data Foundations for the Design and Discovery of Transition Metal Complexes}},
author = {Hannes Kneiding and Bastian Bjerkem Skjelstad and Luca Cibinel and Lucı́a Morán-González and Ainara Nova and David Balcells},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010262/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010262/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Building the Data Foundations for the Design and Discovery of Transition Metal Complexes
AU - Hannes Kneiding
AU - Bastian Bjerkem Skjelstad
AU - Luca Cibinel
AU - Lucı́a Morán-González
AU - Ainara Nova
AU - David Balcells
PY - 2026
DA - 2026/10/12
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010262/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010262/v1
AB - The unique chemistry of transition metal complexes (TMCs) enables several advanced technologies in diverse fields including renewable energies, medicine, and catalysis. Machine learning methods can accelerate their rational design but are bounded by limited dataset sizes, restricted chemical scopes, and insufficient quality. This Perspective surveys the current landscape of TMC datasets and investigates how they seek to address these challenges. The analysis focuses on the data curation processes as well as the scope of the properties provided, and their level of theory. Furthermore, the applications and methodological advancements enabled by TMC datasets are reviewed, including the generative design of metal ligands and the evolutionary optimization of TMCs. The Perspective concludes by outlining future directions, with particular emphasis on extending data to broader chemical scopes including non-equilibrium geometries, establishing community-wide data formats and benchmarks, and developing strategies to effectively integrate heterogeneous data sources.
ER -