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Title: Emerging role of machine learning in light-matter interaction
Authors: Zhou, JJ
Huang, B 
Yan, Z
Bunzli, JCG
Issue Date: 2019
Source: Light : science & applications, 11 Sept. 2019, v. 8, 84, p. 1-7
Abstract: 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.
Publisher: Nature Publishing Group
Journal: Light : science & applications 
ISSN: 2095-5545
EISSN: 2047-7538
DOI: 10.1038/s41377-019-0192-4
Rights: © The Author(s) 2019
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution andreproductionin any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a linktotheCreativeCommons license,and indicate ifchanges were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicatedotherwise in a credit line to the material. Ifmaterial is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtainpermission directly from the copyright holder. To view a copy of this license, visit
The following publication Zhou, J., Huang, B., Yan, Z. et al. Emerging role of machine learning in light-matter interaction. Light Sci Appl 8, 84 (2019), 1-7 is available at
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