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Roadmap on Machinelearning inelectronic structure Heather J. KulikThomas HammerschmidtJonathan Schmidt2022 год

Roadmap on Machine learning in electronic structure
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Аннотация: 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.
Год издания: 2022
Авторы: Heather J. Kulik, Thomas Hammerschmidt, Jonathan Schmidt, Silvana Botti, Miguel A. L. Marques, Mario Boley, Matthias Scheffler, Milica Todorović, Patrick Rinke, Corey Oses, Andriy Smolyanyuk, Stefano Curtarolo, Alexandre Tkatchenko, Albert P. Bartók, Sergei Manzhos, Manabu Ihara, Tucker Carrington, Jörg Behler, Olexandr Isayev, Max Veit, Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti, Kristof T. Schütt, Julia Westermayr, Michael Gastegger, Reinhard J. Maurer, Bhupalee Kalita, Kieron Burke, Ryo Nagai, Ryosuke Akashi, Osamu Sugino, Jan Hermann, Frank Noé, Sebastiano Pilati, Claudia Draxl, Martin Kubáň, Santiago Rigamonti, Markus Scheidgen, Marco Esters, David Hicks, Cormac Toher, Prasanna V. Balachandran, Isaac Tamblyn, Steve Whitelam, Colin Bellinger, Luca M. Ghiringhelli
Издательство: IOP Publishing
Источник: Electronic Structure
Ключевые слова: Machine Learning in Materials Science, Electron and X-Ray Spectroscopy Techniques, X-ray Diffraction in Crystallography
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