An unsupervised shapelet-based method for quantification of nanostructured surface imaging
Computational methods to quantify structure–property relationships of materials are lacking in comparison to the widespread availability of nanostructure image-based characterization techniques. There are several existing quantification methods to characterize self-assembled nanomaterials, however many techniques are infeasible for use in processing large-scale spatial variation and/or require significant manual image pre-processing. The use of shapelet functions to computationally quantify nanostructured materials is a promising approach that is generalized for arbitrary pattern types but the current state of the art, the response distance method, requires user/researcher supervision. This need for supervision, along with the computational complexity of the method, make it infeasible for use in fully-automated large-scale computational analysis of nanostructured surface images. The development of a fully-automated unsupervised method for quantification of nanostructured surfaces would enable the development of broadly applicable structure–property relationships for nanomaterials, leveraging large amounts of previously captured data (so-called meta-analyses). In this work, these issues with the shapelet-based response distance method are resolved through the development and validation of a multi-step machine learning process which enables unsupervised (fully-automated) analysis of nanostructure imaging. The presented method removes the need for any researcher knowledge regarding order symmetries and enables truly large-scale analysis (e.g., meta-analyses) of the vast amount of nanostructure imaging data currently available. The presented unsupervised shapelet-based response distance method also results in at least an order-of-magnitude reduction in computational complexity compared to the existing method, at the cost of some degree of generality. A software implementation of the presented unsupervised method is provided in the open-source shapelets Python package.
Authors
- Matthew Peres Tino (ORCID: https://orcid.org/0009-0005-6832-1761)
- Nasser Mohieddin Abukhdeir
- Cameron Chin
Institutions
- University of Waterloo (CA)
Publication Details
- Journal
- International Journal of Computational Materials Science and Engineering
- Published
- 2025-12-24
- DOI
- https://doi.org/10.1142/s204768412550040x
- Primary Topic
- X-ray Diffraction in Crystallography
- Type
- article
- Field-Weighted Citation Impact
- 0.00