Unsupervised feature recognition in single-molecule break junction data
Author:
Affiliation:
1. Department of Physics
2. Budapest University of Technology and Economics
3. Budafoki ut 8
4. Hungary
5. MTA-BME Condensed Matter Research Group
6. Department of Applied Physics
7. Columbia University
8. New York
9. USA
10. Department of Chemistry
Abstract
A combined principal component and neural network analysis serves as an efficient tool for the unsupervised recognition of unobvious but highly relevant trace classes in single-molecule break junction data.
Funder
Emberi Eroforrások Minisztériuma
Nemzeti Kutatási Fejlesztési és Innovációs Hivatal
Division of Materials Research
Publisher
Royal Society of Chemistry (RSC)
Subject
General Materials Science
Link
http://pubs.rsc.org/en/content/articlepdf/2020/NR/D0NR00467G
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3. Chemical principles of single-molecule electronics
4. Quantum properties of atomic-sized conductors
5. Measurement of Single-Molecule Resistance by Repeated Formation of Molecular Junctions
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