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dc.contributor.authorKulik, H. J.
Hammerschmidt, T.
Schmidt, J.
Botti, S.
Marques, M. A. L.
Boley, M.
Scheffler, M.
Todorović, M.
Rinke, P.
Oses, C.
Smolyanyuk, A.
Curtarolo, S.
Tkatchenko, A.
Bartók, A. P.
Manzhos, S.
Ihara, M.
Carrington, T.
Behler, J.
Isayev, O.
Veit, M.
Grisafi, A.
Nigam, J.
Ceriotti, M.
Schütt, K. T.
Westermayr, J.
Gastegger, M.
Maurer, R. J.
Kalita, B.
Burke, K.
Nagai, R.
Akashi, R.
Sugino, O.
Hermann, J.
Noé, F.
Pilati, S.
Draxl, C.
Kuban, M.
Rigamonti, S.
Scheidgen, M.
Esters, M.
Hicks, D.
Toher, C.
Balachandran, P. V.
Tamblyn, I.
Whitelam, S.
Bellinger, C.
Ghiringhelli, L. M.
dc.date.accessioned 2022-09-29T15:06:28Z
dc.date.available 2022-09-29T15:06:28Z
dc.date.issued 2022
dc.identifier.citation Kulik, H. J., Hammerschmidt, T., Schmidt, J., et al.. "Roadmap on Machine learning in electronic structure." Electronic Structure, 4, no. 2 (2022) IOP Publishing: https://doi.org/10.1088/2516-1075/ac572f.
dc.identifier.urihttps://hdl.handle.net/1911/113432
dc.description.abstract In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.
dc.language.iso eng
dc.publisher IOP Publishing
dc.rights Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title Roadmap on Machine learning in electronic structure
dc.type Journal article
dc.citation.journalTitle Electronic Structure
dc.citation.volumeNumber 4
dc.citation.issueNumber 2
dc.identifier.digital Kulik_2022
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1088/2516-1075/ac572f
dc.type.publication publisher version
dc.citation.articleNumber 023004


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