Overview
The transition metal quantum mechanics RDF (tmQM-RDF) dataset has been introduced in Cibinel et al., 2026. Its main purpose is that of collecting an extensive amount of machine-readable data on a population of transition metal complexes (TMCs). The data have been extracted from the tmQM dataset series, specifically from:
- tmQM (quantum properties; Balcells and Skjelstad, 2020),
- tmQMg (graphs for deep learning; Kneiding et al., 2023),
- tmQMg-L (ligand library for genetic algorithms; Kneiding et al., 2024).
An RDF vocabulary
At the core of this effort lies a dedicated vocabulary, based on the RDF syntax, specifically designed to make chemical and structural information explicit, interpretable, and accessible.
Useful links
Querying
The most valuable contribution of tmQM-RDF is in data accessibility. It is possible to easily execute complex queryes using the SPARQL query language. Such queries can access quantitative properties, qualitative information, chemical structures or any combination of these.
Querying beyond tmQM-RDF
If it is not possible or convenient to retrieve all the desired information directly, the knowledge graph still includes all the relevant keys used by the original datasets to index the data. It follows that more sophisticated investigations can be carried out starting from the response provided by tmQM-RDF.
Data security and scope
The authors confirm that tmQM-RDF has been created using publicly available data and that the resource does not provide any direct access point to the original datasets.
This work has been conducted entirely within the scope of a PhD fellowship at the University of Oslo.
Authors
- Luca Cibinel; University of Oslo
- Trond Linjordet; Hokkaido University
- Johan Pensar; University of Oslo
- David Balcells; University of Oslo
- Riccardo De Bin; University of Oslo
- Basil Ell; Bielefeld University
Points of contact
For general inquiries, contact Luca Cibinel (uio.no); ORCID: 0009-0009-1274-8327 (orcid.org)
For chemistry-related inquiries, contact David Balcells (uio.no).
References
- Balcells, David, and Bastian Bjerkem Skjelstad. 2020. “tmQM Dataset – Quantum Geometries and Properties of 86k Transition Metal Complexes.” Journal of Chemical Information and Modeling 60: 6135–46. DOI: 10.1021/acs.jcim.0c01041.
- Cibinel, Luca, Trond Linjordet, Johan Pensar, David Balcells, Riccardo De Bin, and Basil Ell. 2026. “tmQM-RDF Dataset: A Knowledge Graph Representing Transition Metal Complexes.” Journal of Chemical Information and Modeling 66: 7524-7538. DOI: 10.1021/acs.jcim.6c01281.
- Kneiding, Hannes, Ruslan Lukin, Lucas Lang, et al. 2023. “Deep Learning Metal Complex Properties with Natural Quantum Graphs.” Digital Discovery 2: 618–33. DOI: 10.1039/D2DD00129B.
- Kneiding, Hannes, Ainara Nova, and David Balcells. 2024. “Directional Multiobjective Optimization of Metal Complexes at the Billion-System Scale.” Nature Computational Science 4: 263–73. DOI: 10.1038/s43588-024-00616-5.