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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
Open accessAbstract
Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.
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“Identifying key descriptors or features in a structure-property relationship mapping has been demonstrated as an approach to revealing design principles (18,22,32,52) and enabling iterative design (70).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [18]
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Metadata
- Authors
- Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Kulik, Heather J.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-07-14
- Volume
- 121
- Issue
- 16
- Pages
- 9927-10000
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W3181257149
- Cited by
- 358
- References
- 718
- Citations by year
- 2026: 80 · 2025: 101 · 2024: 64 · 2023: 55 · 2022: 51 · 2021: 7
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Chemical Reviews (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Defense Advanced Research Projects Agency (D18AP00039)
- American Association for the Advancement of Science (Marion Milligan Mason Award)
- Office of Naval Research (N00014-17-1-2956, N00014-18-1-2434, N00014-20-1-2150)
- U.S. Department of Energy (DE-NA0003965, DE-SC0012702, DE-SC0018096, DE-SC0019112)
- Burroughs Wellcome Fund (Career Award at the Scientific Interface 1010755)
- National Science Foundation (CBET-1704266, CBET-1846426, ECCS-1449291)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- transition metal complexes, high-throughput screening, machine learning, structure-property relationships, catalyst discovery, density functional theory, force fields, semiempirical calculations, multi-objective optimization, artificial intelligence, data scarcity, lead compound identification, design rules
- MeSH terms
- Machine Learning, Metals, Transition Elements, Coordination Complexes, High-Throughput Screening Assays
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