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See detailSpeeding up nanoscience and nanotechnology with ultrafast plasmonics
Maccaferri, Nicolò UL; Meuret, Sophie; Kornienko, Nikolay et al

in Nano Letters (2020)

Surface plasmons are collective oscillations of free electrons at the interface between a conducting material and the dielectric environment. These excitations support the formation of strongly enhanced ... [more ▼]

Surface plasmons are collective oscillations of free electrons at the interface between a conducting material and the dielectric environment. These excitations support the formation of strongly enhanced and confined electromagnetic fields. As well, they display fast dynamics lasting tens of femtoseconds and can lead to a strong nonlinear optical response at the nanoscale. Thus, they represent the perfect tool to drive and control fast optical processes, such as ultrafast optical switching, single photon emission, as well as strong coupling interactions to explore and tailor photochemical reactions. In this Virtual Issue, we gather several important papers published in Nano Letters in the past decade reporting studies on the ultrafast dynamics of surface plasmons. [less ▲]

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See detailMachine learning in nanoscience: big data at small scales
Brown, Keith A.; Brittman, Sarah; Maccaferri, Nicolò UL et al

in Nano Letters (2020), 20(1), 2-10

Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with ... [more ▼]

Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with these tools to tackle challenges in many fields. In addition to ML’s advancement of nanoscience, nanoscience provides the foundation for neuromorphic computing hardware to expand the implementation of ML algorithms. In this mini-review, which is not able to be comprehensive, we highlight some recent efforts to connect the ML and nanoscience communities focusing on three types of interaction: (1) using ML to analyze and extract new information from large nanoscience data sets, (2) applying ML to accelerate materials discovery, including the use of active learning to guide experimental design, and (3) the nanoscience of memristive devices to realize hardware tailored for ML. We conclude with a discussion of challenges and opportunities for future interactions between nanoscience and ML researchers. [less ▲]

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