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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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Monitoring the role of site chemistry on the formation energy of perovskites via deep learning analysis of Hirshfeld surfaces
This paper presents a new approach for predicting thermodynamic properties of perovskites that harnesses deep learning and crystal structure fingerprinting based on Hirshfeld surface analysis. It is demonstrated that convolutional neural network methods capture critical features embedded in two-dimensional Hirshfeld surface fingerprints that enable a quantitative assessment of the formation energy of perovskites. Building on our recent work on lattice parameter prediction from Hirshfeld surface calculations, we show how transfer learning can be used to speed up the training of the neural network, allowing multiple properties to be trained using the same feature extraction layers. We also predict formation energies for various perovskite polymorphs, and our predictions are found to give generally improved performance over a well-established graph network method, but with the methods better suited to different types of datasets. Analysis of the structure types within the dataset reveals the Hirshfeld surface-based method to excel for the less symmetric and similar structures, while the graph network performs better for very symmetric and similar structures.
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- Award ID(s):
- 1640867
- PAR ID:
- 10379603
- Date Published:
- Journal Name:
- Journal of Materials Chemistry C
- Volume:
- 9
- Issue:
- 34
- ISSN:
- 2050-7526
- Page Range / eLocation ID:
- 11153 to 11162
- 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/10.1039/d1tc01972d
