Approaches are needed to accelerate the discovery of transition metal complexes (TMCs), which is challenging owing to their vast chemical space. A large dataset of diverse ligands is now introduced and leveraged in a multiobjective genetic algorithm that enables the efficient optimization of TMCs in chemical spaces containing billions of them.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Nandy, A. et al. Computational discovery of transition-metal complexes: from high-throughput screening to machine learning. Chem. Rev. 121, 9927–10000 (2021). A review article that presents machine learning approaches for the computational discovery of TMCs.
Le, T. C. & Winkler, D. A. Discovery and optimization of materials using evolutionary approaches. Chem. Rev. 116, 6107–6132 (2016). A review article that presents the use of evolutionary learning for materials discovery.
Konak, A., Coit, D. W. & Smith, A. E. Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Safety. 91, 992–1007 (2006). A review article that presents multiobjective genetic algorithms and their constituent parts.
Kneiding, H. et al. Deep learning metal complex properties with natural quantum graphs. Digit. Discov. 2, 618–633 (2023). This paper reports the tmQMg dataset, which formed the basis for the ligand dataset leveraged in this work.
Foscato, M., Venkatraman, V. & Jensen, V. R. DENOPTIM: software for computational de novo design of organic and inorganic molecules. J. Chem. Inf. Model. 59, 4077–4082 (2019). This paper reports the DENOPTIM software, which enables the fragment-based evolutionary optimization of molecules.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Kneiding, H. et al. Directional multiobjective optimization of metal complexes at the billion-system scale. Nat. Comput. Sci. https://doi.org/10.1038/s43588-024-00616-5 (2024).
Rights and permissions
About this article
Cite this article
Discovering metal complexes in vast chemical spaces. Nat Comput Sci 4, 259–260 (2024). https://doi.org/10.1038/s43588-024-00618-3
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43588-024-00618-3
