Reference : Machine learning in nanoscience: big data at small scales
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others
Computational Sciences
http://hdl.handle.net/10993/41197
Machine learning in nanoscience: big data at small scales
English
Brown, Keith A. [Boston University]
Brittman, Sarah [U.S. Naval Research Laboratory]
Maccaferri, Nicolò mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Physics and Materials Science Research Unit >]
Jariwala, Deep [University of Pennsylvania - Penn]
Celana, Umberto [IMEC]
2020
Nano Letters
American Chemical Society
20
1
2-10
Yes (verified by ORBilu)
International
1530-6984
1530-6992
Washington
DC
[en] 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.
http://hdl.handle.net/10993/41197
10.1021/acs.nanolett.9b04090
https://pubs.acs.org/doi/10.1021/acs.nanolett.9b04090

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