Deep learning metal complex properties with natural quantum graphs
Open accessPublished version of a preprint
Abstract
Deep graph learning based on electronic structure can contribute to the accelerated discovery of transition metal complexes.
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Cites
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Flagged first, then by citations
- Authors
- Kneiding, H., Lukin, R., Lang, L., Reine, S., Pedersen, T. B., De Bin, R., & Balcells, D.
- Affiliations
- University of Oslo (NO)
- Department of Mathematics, University of Oslo, P. O. Box 1053, Blindern, Oslo, Norway
- ORCID
- Venue
- Digital Discovery
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2023
- Volume
- 2
- Issue
- 3
- Pages
- 618-633
- ISSN
- 2635-098X (ISSN-L)
- OpenAlex
- W4360780118
- Cited by
- 61
- References
- 16
- Citations by year
- 2026: 18 · 2025: 19 · 2024: 14 · 2023: 10
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Digital Discovery (publishedVersion, cc-by-nc)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Funders
- Horizon 2020 (945371, 101025672)
- Norges Forskningsråd (325003, 262695, NN4654K)
- 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, quantum graphs, graph neural networks, electronic structure, deep graph learning
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