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Roadmap on Machine Learning in Electronic Structure

Authors: H. Kulik, T. Hammerschmidt, J. Schmidt, S. Botti, M.A.L. Marques, M. Boley, M. Scheffler, M. Todorović, P. Rinke, C. Oses, A. Smolyanyuk, S. Curtarolo, A. Tkatchenko, A. Bartok, S. Manzhos, M. Ihara, T. Carrington, J. Behler, O. Isayev, M. Veit, A. Grisafi, J. Nigam, M. Ceriotti, K.T. Schütt, J. Westermayr, M. Gastegger, R. Maurer, B. Kalita, K. Burke, R. Nagai, R. Akashi, O. Sugino, J. Hermann, F. Noé, S. Pilati, C. Draxl, M. Kuban, S. Rigamonti, M. Scheidgen, M. Esters, D. Hicks, C. Toher, P. Balachandran, I. Tamblyn, S. Whitelam, C. Bellinger, and L.M. Ghiringhelli

Ref.: accepted for publication in Electron. Struct. (2022)

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.

Citations: 5 (Google scholar)

DOI: 10.1088/2516-1075/ac572f

Bibtex:

@article{Kulik_2022,
	doi = {10.1088/2516-1075/ac572f},
	url = {https://doi.org/10.1088%2F2516-1075%2Fac572f},
	year = 2022,
	month = {feb},
	publisher = {{IOP} Publishing},
	author = {Heather Kulik and Thomas Hammerschmidt and Jonathan Schmidt and Silvana Botti and Miguel A. L. Marques and Mario Boley and Matthias Scheffler and Milica Todorovi{\'{c}} and Patrick Rinke and Corey Oses and Andriy Smolyanyuk and Stefano Curtarolo and Alexandre Tkatchenko and Albert Bartok and Sergei Manzhos and Manabu Ihara and Tucker Carrington and Jörg Behler and Olexandr Isayev and Max Veit and Andrea Grisafi and Jigyasa Nigam and Michele Ceriotti and Kristoff T Schütt and Julia Westermayr and Michael Gastegger and Reinhard Maurer and Bhupalee Kalita and Kieron Burke and Ryo Nagai and Ryosuke Akashi and Osamu Sugino and Jan Hermann and Frank No{\'{e}} and Sebastiano Pilati and Claudia Draxl and Martin Kuban and Santiago Rigamonti and Markus Scheidgen and Marco Esters and David Hicks and Cormac Toher and Prasanna Balachandran and Isaac Tamblyn and Stephen Whitelam and Colin Bellinger and Luca M. Ghiringhelli},
	title = {Roadmap on Machine Learning in Electronic Structure},
	journal = {Electronic Structure}
}