Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)

Roadmap on Machine learning in electronic structure




Julkaisun tekijätKulik 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.

KustantajaInstitute of Physics

Julkaisuvuosi2022

JournalElectronic Structure

Tietokannassa oleva lehden nimiElectronic Structure

Artikkelin numero023004

Volyymi4

Julkaisunumero2

eISSN2516-1075

DOIhttp://dx.doi.org/10.1088/2516-1075/ac572f

Verkko-osoitehttps://iopscience.iop.org/article/10.1088/2516-1075/ac572f

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/176607590


Tiivistelmä

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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Last updated on 2023-24-02 at 15:14