Emerging role of machine learning in light-matter interaction

Zhou, J; Huang, B; Yan, Z; Bünzli, JCG

Emerging role of machine learning in light-matter interaction

Zhou, J

Huang, B

Yan, Z

Bünzli, JCG

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Publication Type:: Journal Article
Citation:: Light: Science and Applications, 2019, 8 (1)
Issue Date:: 2019-12-01

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Field	Value	Language
dc.contributor.author	Zhou, J https://orcid.org/0000-0002-0605-5745	en_US
dc.contributor.author	Huang, B https://orcid.org/0000-0002-2526-2002	en_US
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.contributor.author	Bünzli, JCG	en_US
dc.date.available	2019-08-06	en_US
dc.date.issued	2019-12-01	en_US
dc.identifier.citation	Light: Science and Applications, 2019, 8 (1)	en_US
dc.identifier.issn	2095-5545	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136473
dc.description.abstract	© 2019, The Author(s). Machine learning has provided a huge wave of innovation in multiple fields, including computer vision, medical diagnosis, life sciences, molecular design, and instrumental development. This perspective focuses on the implementation of machine learning in dealing with light-matter interaction, which governs those fields involving materials discovery, optical characterizations, and photonics technologies. We highlight the role of machine learning in accelerating technology development and boosting scientific innovation in the aforementioned aspects. We provide future directions for advanced computing techniques via multidisciplinary efforts that can help to transform optical materials into imaging probes, information carriers and photonics devices.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE180100669
dc.relation.ispartof	Light: Science and Applications	en_US
dc.relation.isbasedon	10.1038/s41377-019-0192-4	en_US
dc.title	Emerging role of machine learning in light-matter interaction	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	8	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0205 Optical Physics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - IBMD - Initiative for Biomedical Devices
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

© 2019, The Author(s). Machine learning has provided a huge wave of innovation in multiple fields, including computer vision, medical diagnosis, life sciences, molecular design, and instrumental development. This perspective focuses on the implementation of machine learning in dealing with light-matter interaction, which governs those fields involving materials discovery, optical characterizations, and photonics technologies. We highlight the role of machine learning in accelerating technology development and boosting scientific innovation in the aforementioned aspects. We provide future directions for advanced computing techniques via multidisciplinary efforts that can help to transform optical materials into imaging probes, information carriers and photonics devices.

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http://hdl.handle.net/10453/136473