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Light-Induced In Situ Transmission Electron Microscopy for Observation of the Liquid-Soft Matter Interaction
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Emerging role of machine learning in light-matter interaction
Jiajia Zhou1, Bolong Huang2, Zheng Yan3
11Faculty of Science, Institute for Biomedical Materials and Devices, University of Technology, Sydney, NSW 2007 Australia.
Light, Science & Applications
|October 25, 2019
Summary
Machine learning accelerates innovation in light-matter interactions for materials discovery and photonics. Advanced computing techniques will further transform optical materials for diverse applications.
Area of Science:
- Physics
- Materials Science
- Computer Science
Background:
- Machine learning (ML) drives innovation across various scientific domains.
- Light-matter interaction is fundamental to materials discovery, optical characterizations, and photonics.
Purpose of the Study:
- To explore the application of ML in understanding and manipulating light-matter interactions.
- To highlight ML's role in accelerating technological development and scientific discovery in optics and photonics.
Main Methods:
- Review and synthesis of current research on ML applications in light-matter interaction.
- Identification of key areas where ML impacts optical characterizations and materials discovery.
Main Results:
- ML significantly enhances the speed and efficiency of materials discovery and optical characterization.
- ML facilitates the development of novel photonics technologies and devices.
Conclusions:
- ML is a transformative tool for advancing research in light-matter interaction.
- Multidisciplinary efforts leveraging advanced computing can unlock new potentials for optical materials in imaging, information, and devices.
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