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Exploiting machine learning in multiscale modelling of materials
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Anand, G., Ghosh, Swarnava, Zhang, Liwei, Anupam, Angesh, Freeman, Colin L., Ortner, Christoph, Eisenbach, Markus and Kermode, James R. (2023) Exploiting machine learning in multiscale modelling of materials. Journal of The Institution of Engineers (India) : Series D, 104 . pp. 867-877. doi:10.1007/s40033-022-00424-z ISSN 2250-2122.
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Official URL: http://dx.doi.org/10.1007/s40033-022-00424-z
Abstract
Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Machine learning, Materials science -- Mathematical models, Multiscale modeling | ||||||||
Journal or Publication Title: | Journal of The Institution of Engineers (India) : Series D | ||||||||
Publisher: | Springer | ||||||||
ISSN: | 2250-2122 | ||||||||
Official Date: | December 2023 | ||||||||
Dates: |
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Volume: | 104 | ||||||||
Page Range: | pp. 867-877 | ||||||||
DOI: | 10.1007/s40033-022-00424-z | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Re-use Statement: | The authors are thankful to UKIERI and DST for funding the Partnership Development Workshop. The authors are also thankful to US-DOE-ORNL for funding. This research used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. The authors acknowledge the Network Builder Grant from Cardiff Met University. | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Copyright Holders: | © The Institution of Engineers (India) 2022 | ||||||||
Date of first compliant deposit: | 29 November 2022 | ||||||||
Date of first compliant Open Access: | 29 November 2022 | ||||||||
RIOXX Funder/Project Grant: |
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