Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

Taylor, Michael G.; Yang, Tzuhsiung; Lin, Sean; Nandy, Aditya; Janet, Jon Paul; Duan, Chenru; Kulik, Heather J.

doi:10.1021/acs.jpca.0c01458

Cited by 81Magnetism in coordination complexesMachine Learning in Materials ScienceAdvanced Chemical Physics Studies

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

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Taylor, Michael G., Yang, Tzuhsiung, Lin, Sean, Nandy, Aditya, Janet, Jon Paul, Duan, Chenru, Kulik, Heather J.·2020·The Journal of Physical Chemistry A

DOI 10.1021/acs.jpca.0c01458PMC PMC7311053

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Abstract

Determination of ground-state spins of open-shell transition-metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition-metal complexes. We first identify the limits of distance-based heuristics from distributions of metal–ligand bond lengths of over 2000 unique mononuclear Fe(II)/Fe(III) transition-metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal–ligand bond lengths and classify experimental ground-state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80–90%) of structures. We demonstrate the utility of the ANN by data-mining the literature for spin-crossover (SCO) complexes, which have experimentally observed temperature-dependent geometric structure changes, by correctly assigning almost all (>95%) spin states in the 46 Fe(II) SCO complex set. This approach represents a promising complement to more conventional energy-based spin-state assignment from electronic structure theory at the low cost of a machine learning model.

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An ANN trained on topological descriptors to predict DFT metal-ligand bond lengths was used to classify experimental spin states and even to correct DFT-based energetic predictions of spin states.
“An ANN trained on topological descriptors to predict DFT metal-ligand bond lengths (52) was used to classify experimental spin states (85) and even to correct DFT-based energetic predictions of spin states.”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [85]

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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
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Machine Learning for the Discovery, Design, and Engineering of Materials.
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Authors: Taylor, M. G., Yang, T., Lin, S., Nandy, A., Janet, J. P., Duan, C., & Kulik, H. J.
Affiliations: Massachusetts Institute of Technology (US)
Corresponding author: Kulik, Heather J.
ORCID: Taylor, Michael G.
Yang, Tzuhsiung
Nandy, Aditya
Janet, Jon Paul
Duan, Chenru
Kulik, Heather J.
Venue: The Journal of Physical Chemistry A
Publisher: American Chemical Society (ACS)
Published: 2020-03-28
Volume: 124
Issue: 16
Pages: 3286-3299
ISSN: 1089-5639 (ISSN-L)
1520-5215
DOI: 10.1021/acs.jpca.0c01458
PMC: PMC7311053
OpenAlex: W3013702686
Cited by: 81
References: 110
Citations by year: 2026: 5 · 2025: 9 · 2024: 11 · 2023: 21 · 2022: 9 · 2021: 19 · 2020: 7
FWCI: 9.64
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html (vor, 12 d)
Locations: The Journal of Physical Chemistry A (publishedVersion, cc-by)
ChemRxiv (acceptedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by)
Figshare (submittedVersion, cc-by-nc-nd)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders: American Association for the Advancement of Science (Marion Milligan Mason Award)
Office of Naval Research (N00014-17-1-2956, N00014-18-1-2434)
U.S. Department of Energy (DE-SC0018096)
Burroughs Wellcome Fund (Career Award at the Scientific Interface)
National Science Foundation (CBET-1846426)
Topics: Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Magnetism in coordination complexes
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
Keywords: transition metal complexes, artificial neural networks, metal-ligand bond lengths, spin-crossover complexes, crystal structure, open-shell systems, hybrid density functional theory

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