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Cited by 32Crystal structures of chemical compoundsSupramolecular Chemistry and ComplexesInorganic and Organometallic Chemistry

Conformation of tripod Metal Templates in CH3C(CH2PPh2)3MLn (n = 2, 3): Neural Networks in Conformational Analysis

Beyreuther, Stefan, Hunger, Johannes, Huttner, Gottfried, Mann, Susanne, Zsolnai, Laszlo·1996·Chemische Berichte
DOI 10.1002/cber.19961290704
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Abstract

The conformational space spanned by tripod metal templates CH 3 C(CH 2 Ph 2)3 M is analysed on the basis of the solid‐state structures of 82 tripod Co templates in compounds tripod CoL 2 and tripod CoL 3. Systematic analysis, including the techniques of conformational space group scatter graphs, principal‐component analysis, and partial least squares, reveals a series of basic regularities: The torsion of the phenyl groups is strongly linked to the torsional skew of the bicyclooctane‐type framework of the chelate cage. For one sense of this skew there are two classes of low‐energy conformation that differ by the helicity of the phenyl arrangement and by the degree of torsional skew in the chelate backbone. From the scatter graphs it is evident that a change in helicity may occur by one‐ or by two‐ring flip mechanisms. The basic regularities found by the above methods are also evident from the analysis of the same data by a neural network approach. The fact that these regularities are found for tripod CoL 2 and tripod CoL 3, irrespective of the widely different coligands L and crystal environments, means that the conformation of the tripod metal templates is governed by the forces acting within them and not so much by the forces imposed on them by their individual chemical or crystal environment. It is shown that the classifications, although derived from a data basis only containing Co compounds, are characteristic for tripod metal templates irrespective of the specific metal involved.

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  • ML models, analysis of their key features, and dimensionality reduction represent important tools for building structure-property relationships and mapping chemical space.

    “ML models, analysis of their key features (32,52), and dimensionality reduction (i.e., unsupervised learning) (53-56) represent important tools for building structure-property relationships and mapping chemical space (57).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [55]

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Authors
Beyreuther, S., Hunger, J., Huttner, G., Mann, S., & Zsolnai, L.
Affiliations
Heidelberg University (DE)
ORCID
Hunger, Johannes
Venue
Chemische Berichte
Publisher
Wiley
Published
1996-07
Volume
129
Issue
7
Pages
745-757
ISSN
0009-2940 (ISSN-L)
DOI
10.1002/cber.19961290704
OpenAlex
W1987075193
Cited by
32
References
59
Citations by year
2022: 1 · 2021: 1 · 2013: 2 · 2012: 1
FWCI
5.22
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 3495 d)
Locations
Chemische Berichte (publishedVersion)
Topics
  • Physical Sciences › Chemistry › Inorganic Chemistry › Crystal structures of chemical compounds
  • Physical Sciences › Chemistry › Organic Chemistry › Supramolecular Chemistry and Complexes
  • Physical Sciences › Chemistry › Organic Chemistry › Inorganic and Organometallic Chemistry
Keywords
cobalt complexes, conformational analysis, helicity, artificial neural networks, principal component analysis, partial least squares

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