21 11月 2022 gabriels
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.


Roadmap on Machine learning in electronic structure

H J 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 P Bartók, 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 J 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 V Balachandran, I Tamblyn, S Whitelam, C Bellinger and L M Ghiringhelli


Electronic Structure

  • Electronic Structure(EST)是一本新发表的多学科期刊,覆盖电子结构研究的理论和实验工作,包括新方法的开发。EST是第一本致力于服务电子结构领域的期刊,涵盖材料学、物理学、化学和生物学。除了原创性研究外,EST还发表专题综述、专刊和技术笔记。