Exploiting machine learning in multiscale modelling of materials. Anand, G., Ghosh, S., Zhang, L., Anupam, A., Freeman, C. L., Ortner, C., Eisenbach, M., & Kermode, J. R. Journal of The Institution of Engineers (India) : Series D, 104:867–877, Springer, December, 2023. 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.![Exploiting machine learning in multiscale modelling of materials [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{warwick171506,
note = {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.},
title = {Exploiting machine learning in multiscale modelling of materials},
month = {December},
year = {2023},
journal = {Journal of The Institution of Engineers (India) : Series D},
pages = {867--877},
publisher = {Springer},
volume = {104},
doi = {10.1007/s40033-022-00424-z},
issn = {2250-2122},
url = {http://dx.doi.org/10.1007/s40033-022-00424-z},
author = {Anand, G. and Ghosh, Swarnava and Zhang, Liwei and Anupam, Angesh and Freeman, Colin L. and Ortner, Christoph and Eisenbach, Markus and Kermode, James R.},
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.
}
}
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The authors acknowledge the Network Builder Grant from Cardiff Met University.","title":"Exploiting machine learning in multiscale modelling of materials","month":"December","year":"2023","journal":"Journal of The Institution of Engineers (India) : Series D","pages":"867–877","publisher":"Springer","volume":"104","doi":"10.1007/s40033-022-00424-z","issn":"2250-2122","url":"http://dx.doi.org/10.1007/s40033-022-00424-z","author":[{"propositions":[],"lastnames":["Anand"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["Ghosh"],"firstnames":["Swarnava"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Liwei"],"suffixes":[]},{"propositions":[],"lastnames":["Anupam"],"firstnames":["Angesh"],"suffixes":[]},{"propositions":[],"lastnames":["Freeman"],"firstnames":["Colin","L."],"suffixes":[]},{"propositions":[],"lastnames":["Ortner"],"firstnames":["Christoph"],"suffixes":[]},{"propositions":[],"lastnames":["Eisenbach"],"firstnames":["Markus"],"suffixes":[]},{"propositions":[],"lastnames":["Kermode"],"firstnames":["James","R."],"suffixes":[]}],"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. ","bibtex":"@article{warwick171506,\n note = {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.},\n title = {Exploiting machine learning in multiscale modelling of materials},\n month = {December},\n year = {2023},\n journal = {Journal of The Institution of Engineers (India) : Series D},\n pages = {867--877},\n publisher = {Springer},\n volume = {104},\n doi = {10.1007/s40033-022-00424-z},\n issn = {2250-2122},\n url = {http://dx.doi.org/10.1007/s40033-022-00424-z},\n author = {Anand, G. and Ghosh, Swarnava and Zhang, Liwei and Anupam, Angesh and Freeman, Colin L. and Ortner, Christoph and Eisenbach, Markus and Kermode, James R.},\n 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.\r\n\r\n}\n}\n\n","author_short":["Anand, G.","Ghosh, S.","Zhang, L.","Anupam, A.","Freeman, C. 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