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Roadmap on Machine learning in electronic structure

Publication ,  Journal Article
Kulik, HJ; Hammerschmidt, T; Schmidt, J; Botti, S; Marques, MAL; Boley, M; Scheffler, M; Todorović, M; Rinke, P; Oses, C; Smolyanyuk, A ...
Published in: Electronic Structure
June 1, 2022

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.

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Published In

Electronic Structure

DOI

EISSN

2516-1075

Publication Date

June 1, 2022

Volume

4

Issue

2
 

Citation

APA
Chicago
ICMJE
MLA
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Kulik, H. J., Hammerschmidt, T., Schmidt, J., Botti, S., Marques, M. A. L., Boley, M., … Ghiringhelli, L. M. (2022). Roadmap on Machine learning in electronic structure. Electronic Structure, 4(2). https://doi.org/10.1088/2516-1075/ac572f
Kulik, H. J., T. Hammerschmidt, J. Schmidt, S. Botti, M. A. L. Marques, M. Boley, M. Scheffler, et al. “Roadmap on Machine learning in electronic structure.” Electronic Structure 4, no. 2 (June 1, 2022). https://doi.org/10.1088/2516-1075/ac572f.
Kulik HJ, Hammerschmidt T, Schmidt J, Botti S, Marques MAL, Boley M, et al. Roadmap on Machine learning in electronic structure. Electronic Structure. 2022 Jun 1;4(2).
Kulik, H. J., et al. “Roadmap on Machine learning in electronic structure.” Electronic Structure, vol. 4, no. 2, June 2022. Scopus, doi:10.1088/2516-1075/ac572f.
Kulik HJ, Hammerschmidt T, Schmidt J, Botti S, Marques MAL, Boley M, Scheffler M, Todorović M, Rinke P, Oses C, Smolyanyuk A, Curtarolo S, Tkatchenko A, Bartók AP, Manzhos S, Ihara M, Carrington T, Behler J, Isayev O, Veit M, Grisafi A, Nigam J, Ceriotti M, Schütt KT, Westermayr J, Gastegger M, Maurer RJ, 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 PV, Tamblyn I, Whitelam S, Bellinger C, Ghiringhelli LM. Roadmap on Machine learning in electronic structure. Electronic Structure. 2022 Jun 1;4(2).

Published In

Electronic Structure

DOI

EISSN

2516-1075

Publication Date

June 1, 2022

Volume

4

Issue

2