Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions
Name
acs.jpca.0c01458.pdf
Description
Published version
Size
2.8 MB
Format
Adobe PDF
Checksum (MD5)
66c00351e89da0b1312a21dfee19a2f1
Author(s)
Taylor, Michael D.
Yang, Tzuhsiung
Lin, Sean
Nandy, Aditya
Janet, Jon Paul
Duan, Chenru
Kulik, Heather Janine
Date Issued
March 2020
Journal
The journal of physical chemistry. A
Publisher
American Chemical Society (ACS)
Citation
Taylor, Michael G. et al. “Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions” The journal of physical chemistry. A, vol. 124, no. 16, 2020, pp. 3286-3299 © 2020 The Author(s)
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1021/acs.jpca.0c01458
