Article (Scientific journals)
Machine learning in nanoscience: big data at small scales
Brown, Keith A.; Brittman, Sarah; Maccaferri, Nicolò et al.
2020In Nano Letters, 20 (1), p. 2-10
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Abstract :
[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.
Disciplines :
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others
Author, co-author :
Brown, Keith A.;  Boston University
Brittman, Sarah;  U.S. Naval Research Laboratory
Maccaferri, Nicolò ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Physics and Materials Science Research Unit
Jariwala, Deep;  University of Pennsylvania - Penn
Celano, Umberto;  imec
External co-authors :
yes
Language :
English
Title :
Machine learning in nanoscience: big data at small scales
Publication date :
2020
Journal title :
Nano Letters
ISSN :
1530-6992
Publisher :
American Chemical Society, Washington, United States - District of Columbia
Volume :
20
Issue :
1
Pages :
2-10
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
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