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Group 6: Challenge: Task 3 - detect defects in electron microscopy images

Group 6: Challenge: Task 3 - detect defects in electron microscopy images
Group 6: Challenge: Task 3 - detect defects in electron microscopy images
Graphene is an exotic nanomaterial consisting of a single layer of carbon atoms arranged in a two-dimensional honeycomb lattice (Figure 1). Since its rediscovery and isolation from graphite by tape in 2009, [1] graphene has aroused intensive interest in the worldwide and in numerous research areas, due to its extraordinary mechanical, thermal, optical, electronic, and other properties. The discoverer Geim and Novoselov were awarded the Nobel Prize in Physics in 2010. Although graphene samples with high perfection have such outstanding performance, the defects in graphene are inevitable during the growth and processing, and can significantly affect the properties. Thus, it is important to figure out the amount, position, size and type of defects in a graphene sample. Machine learning, an emerging data-driven approach, offers a highly efficient solution to learning hidden patterns or classifying anomalies from complex data. In this work, we applied machine learning to detect the defects in the electronic microscopic images of graphene samples and compared the performance of several different machine learning algorithms. As the data set contained significantly fewer defect examples than perfect ones, only methods suitable for use with imbalanced data were considered.
AI3SD, Machine Learning, Summer School
4
University of Southampton
Dickson, Robert
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Honore, Ben
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Lin, Hai
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Pirie, Rachael
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Frey, Jeremy G.
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Niranjan, Mahesan
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Kanza, Samantha
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Dickson, Robert
57741a93-39af-471c-80cd-318dd3316a19
Honore, Ben
17bf175e-5e33-4409-8f27-42a5e7fb377f
Lin, Hai
b7b2c9f9-ada4-46ae-9527-f79a13b50a18
Pirie, Rachael
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Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
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Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420

Dickson, Robert, Honore, Ben, Lin, Hai and Pirie, Rachael , Frey, Jeremy G., Niranjan, Mahesan and Kanza, Samantha (eds.) (2022) Group 6: Challenge: Task 3 - detect defects in electron microscopy images (AI4SD-Machine-Learning-Summer-School, 4) University of Southampton 14pp. (doi:10.5258/SOTON/AI3SD0247).

Record type: Monograph (Project Report)

Abstract

Graphene is an exotic nanomaterial consisting of a single layer of carbon atoms arranged in a two-dimensional honeycomb lattice (Figure 1). Since its rediscovery and isolation from graphite by tape in 2009, [1] graphene has aroused intensive interest in the worldwide and in numerous research areas, due to its extraordinary mechanical, thermal, optical, electronic, and other properties. The discoverer Geim and Novoselov were awarded the Nobel Prize in Physics in 2010. Although graphene samples with high perfection have such outstanding performance, the defects in graphene are inevitable during the growth and processing, and can significantly affect the properties. Thus, it is important to figure out the amount, position, size and type of defects in a graphene sample. Machine learning, an emerging data-driven approach, offers a highly efficient solution to learning hidden patterns or classifying anomalies from complex data. In this work, we applied machine learning to detect the defects in the electronic microscopic images of graphene samples and compared the performance of several different machine learning algorithms. As the data set contained significantly fewer defect examples than perfect ones, only methods suitable for use with imbalanced data were considered.

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Published date: 1 July 2022
Venue - Dates: AI4SD Machine Learning Summer School, University of Southampton, Southampton, United Kingdom, 2022-06-20 - 2022-06-24
Keywords: AI3SD, Machine Learning, Summer School

Identifiers

Local EPrints ID: 470693
URI: http://eprints.soton.ac.uk/id/eprint/470693
PURE UUID: 09a5be45-98b1-4918-9b44-87c1dd91826d
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489

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Date deposited: 18 Oct 2022 16:37
Last modified: 19 Oct 2022 01:55

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Contributors

Author: Robert Dickson
Author: Ben Honore
Author: Hai Lin
Author: Rachael Pirie
Editor: Jeremy G. Frey ORCID iD
Editor: Mahesan Niranjan ORCID iD
Editor: Samantha Kanza ORCID iD

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