Machine learning for materials characterisation
- Submission status
- Closed
- Submission deadline
In the last decade we have achieved significant advances in machine learning techniques, which have been widely exploited in a number of applications including biomedical, data and image processing, and materials science. In these applications it’s particularly valuable to be able to process large amounts of data and images in a standardised method, and using machine learning techniques to achieve this can dramatically reduce the drain on resources – e.g. time, computing power, expert man-hours.
In materials science, and in particular when applied to X-ray and neutron scattering techniques, machine learning has been beneficial in discovery, optimisation, and characterisation of new materials. Scattering simulations that are traditionally performed using Monte Carlo techniques (which are time and processing heavy), can be much improved through the use of well-validated machine learning techniques.
This Collection reports the latest advances in machine learning techniques for materials.
Editors
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Dimitrios N. Argyriou
European Spallation Source ERIC, Sweden
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Heloisa N. Bordallo
Niels Bohr Institute – University of Copenhagen, Denmark
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Gowri Srinivasan
Los Alamos National Laboratory, United States
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Veera Sundararaghavan
University of Michigan – Ann Arbor, United States
Collections articles undergo Scientific Reports' standard peer review process and are subject to all of the journal’s standard policies. This includes the journal’s policy on competing interests. The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.
This Collection has not been supported by sponsorship.
