Cited by 359Machine Learning in Materials ScienceComputational Drug Discovery MethodsAdvanced Chemical Physics Studies

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Open access
Nandy, Aditya, Duan, Chenru, Taylor, Michael G., Liu, Fang, Steeves, Adam H., Kulik, Heather J.·2021·Chemical Reviews

Abstract

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.

Integrity signals

Registry

Crossref · 10.1021/acs.chemrev.1c00347

Cites

716 of 716 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Identifying key descriptors or features in a structure-property relationship mapping has been demonstrated as an approach to revealing design principles and enabling iterative design.

    “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).”

Cites

1 retracted

Flagged first, then by citations

2 of the 718 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

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
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
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
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › 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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review