Automated Searching and Identification of Self-Organized Nanostructures

Gordon, Oliver M.; Hodgkinson, Jo E. A.; Farley, Steff M.; Hunsicker, Eugénie L.; Moriarty, Philip J.

Abstract

Currently, researchers spend significant time manually searching through large volumes of data produced during scanning probe imaging to identify specific patterns and motifs formed via self-assembly and self-organization. Here, we use a combination of Monte Carlo simulations, general statistics, and machine learning to automatically distinguish several spatially correlated patterns in a mixed, highly varied data set of real AFM images of self-organized nanoparticles. We do this regardless of feature-scale and without the need for manually labeled training data. Provided that the structures of interest can be simulated, the strategy and protocols we describe can be easily adapted to other self-organized systems and data sets.

Citation

Gordon, O. M., Hodgkinson, J. E. A., Farley, S. M., Hunsicker, E. L., & Moriarty, P. J. (2020). Automated Searching and Identification of Self-Organized Nanostructures. Nano Letters, 20(10), 7688-7693. https://doi.org/10.1021/acs.nanolett.0c03213

• Journal Article Type: Article
• Acceptance Date: Aug 31, 2020
• Online Publication Date: Aug 31, 2020
• Publication Date: Oct 14, 2020
• Deposit Date: Sep 14, 2020
• Publicly Available Date: Sep 1, 2021
• Journal: Nano Letters
• Print ISSN: 1530-6984
• Electronic ISSN: 1530-6992
• Publisher: American Chemical Society
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 10
• Pages: 7688-7693
• DOI: https://doi.org/10.1021/acs.nanolett.0c03213
• Keywords: Mechanical Engineering; General Materials Science; Bioengineering; General Chemistry; Condensed Matter Physics
• Public URL: https://nottingham-repository.worktribe.com/output/4886381
• Publisher URL: https://pubs.acs.org/doi/10.1021/acs.nanolett.0c03213