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
Submitting a paper for consideration
To submit your manuscript for consideration at Scientific Reports as part of this Collection, please follow the steps detailed on this page. On the first page of our online submission system, under “I’m submitting:” select the option “any other article type”. Once logged in you can submit your manuscript to a Collection by selecting “Guest Edited Collection”, under the “Choose the appropriate manuscript type” message, and clicking “Continue”. Then when filling out the manuscript information, select the "Machine learning for materials characterisation" Collection from the alphabetical list on the “Springer Nature Subject Category” tab. Authors should express their interest in the Collection in their cover letter.
Accepted papers are published on a rolling basis as soon as they are ready.
In addition to papers on Machine learning for materials characterisation, Scientific Reports welcomes all original research in the field of Materials science. To browse our latest articles in Materials science click here.
