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We demonstrate the use of non-linear manifold learning methods to map the connectivity and extent of similarity between diverse metal-organic framework (MOF) structures in terms of their surface areas by taking into account both crystallographic and electronic structure information. The fusing of geometric and chemical bonding information is accomplished by using 3-dimensional Hirshfeld surfaces of MOF structures, which encode both chemical bonding and molecular geometry information. A comparative analysis of the geometry of Hirshfeld surfaces is mapped into a low dimensional manifold through a graph network where each node corresponds to a different compound. By examining nearest neighbor connections, we discover structural and chemical correlations among MOF structures that would not have been discernible otherwise. Examples of the types of information that can be uncovered using this approach are given.
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Quantifying and Understanding Errors in Molecular Geometries
Electronic structure calculations are ubiquitous in most branches of chemistry, but all have errors in both energies and equilibrium geometries. Quantifying errors in possibly dozens of bond angles and bond lengths is a Herculean task. A single natural measure of geometric error is introduced, the geometry energy offset (GEO). GEO links many disparate aspects of geometry errors: a new ranking of different methods, quantitative insight into errors in specific geometric parameters, and insight into trends with different methods. GEO can also reduce the cost of high-level geometry optimizations and shows when geometric errors distort the overall error of a method. Results, including some surprises, are given for both covalent and weak interactions.
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- Award ID(s):
- 1856165
- PAR ID:
- 10220448
- Date Published:
- Journal Name:
- The journal of physical chemistry letters
- Issue:
- 0
- ISSN:
- 1948-7185
- Page Range / eLocation ID:
- 9957–9964
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/acs.jpclett.0c03034
