The Disruptive Cross-Fertilization of Nanotechnology and Machine Learning
A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Theory and Simulation of Nanostructures".
Deadline for manuscript submissions: closed (10 February 2024) | Viewed by 262
Special Issue Editor
Interests: machine learning for nanostructure design; machine learning for data analysis; metamaterials; metasurfaces; metalenses; sensing; spectroscopy; imaging; communication
Special Issue Information
Dear Colleagues,
Nanotechnology and machine learning are amazing technologies with a very high innovation impact on multiple domains such as healthcare, communication, agriculture, food, materials, and energy, to cite a few. Advances from both technologies are already driving us towards a new era of achievements and opportunities. In the future, we imagine an era of the disruptive cross-fertilization of nanotechnology and machine learning.
This Special Issue was launched to provide an opportunity for researchers working on the cross-fertilization of nanotechnology and machine learning to highlight their most recent exciting developments and solutions to the current bottlenecks and disruptive breakthroughs to promote practical and innovative applications and shed light upon new developments. This can happen at multiple levels; for example, from the (electromagnetic, electronic, chemical, and multiphysical) design of nanodevices and nanomaterials to the analysis of data collected by advanced nanodevices/nanomaterials-based setups. Sensing, spectroscopy, imaging, and communication are clearly application domains where this disruptive technology cross-fertilization can have a profound, game-changing impact.
Potential topics for this Special Issue include (but are not limited to):
- Machine learning for nanodevices and nanomaterials design;
- Machine learning for data analysis (nanodevices/nanomaterials-based setups);
- Nanomaterials;
- Metamaterials;
- Metasurfaces;
- Nanostructure-based surface-enhanced Raman scattering (SERS);
- Nanostructure-based surface-enhanced infrared absorption (SEIRA);
- Metalenses;
- Nanodevices;
- Sensing, spectroscopy, imaging, and communication.
Prof. Dr. Francesco Ferranti
Guest Editor
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Keywords
- machine learning for nanodevices and nanomaterials design
- machine learning for data analysis (nanodevices/nanomaterials-based setups)
- nanomaterials
- metamaterials
- metasurfaces
- nanostructure-based surface-enhanced Raman scattering (SERS)
- nanostructure-based surface-enhanced infrared absorption (SEIRA)
- metalenses
- nanodevices
- sensing
- spectroscopy
- imaging
- communication
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