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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.: Electron. Struct. 4, 023004 (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: 32 (Google scholar)

DOI: 10.1088/2516-1075/ac572f



	doi = {10.1088/2516-1075/ac572f},
	url = {https://doi.org/10.1088%2F2516-1075%2Fac572f},
	year = 2022,
	month = {jun},
	publisher = {{IOP} Publishing},
	volume = {4},
	number = {2},
	pages = {023004},
	author = {H J Kulik and T Hammerschmidt and J Schmidt and S Botti and M A L Marques and M Boley and M Scheffler and M Todorovi{\'{c}} and P Rinke and C Oses and A Smolyanyuk and S Curtarolo and A Tkatchenko and A P Bart{\'{o}}k and S Manzhos and M Ihara and T Carrington and J Behler and O Isayev and M Veit and A Grisafi and J Nigam and M Ceriotti and K T Schütt and J Westermayr and M Gastegger and R J Maurer and B Kalita and K Burke and R Nagai and R Akashi and O Sugino and J Hermann and F No{\'{e}} and S Pilati and C Draxl and M Kuban and S Rigamonti and M Scheidgen and M Esters and D Hicks and C Toher and P V Balachandran and I Tamblyn and S Whitelam and C Bellinger and L M Ghiringhelli},
	title = {Roadmap on Machine learning in electronic structure},
	journal = {Electronic Structure}