TY - JOUR A1 - Hickel, Tilmann A1 - Divinski, S. A1 - Starikov, S. A1 - Soisson, F. A1 - Mény, C. A1 - Hegde, O. A1 - Gerlitz, M. A1 - Magnifouet, G. A1 - Schneider, A. A1 - Barreteau, C. A1 - Mirebeau, I. A1 - Tran, V.T. A1 - Förster, G. A1 - Front, A. A1 - Egorov, A. A1 - Wilde, G. A1 - Amara, H. A1 - Hammerschmidt, T. A1 - Mrovec, M. A1 - Pierron-Bohnes, V. A1 - Drautz, R. A1 - Fu, C. T1 - Magnetism in iron alloys: methodological advances for thermodynamics, defects, and kinetics N2 - Steels are among the technologically and economically most relevant materials. Key innovations in important sectors of human society such as mobility, energy and safety, are currently based on alloying of Fe with other transition-metal elements such as Mn, Cr, or Co. Due to strong impacts and conceptual challenges related to magnetism, however, the fundamental understanding and the ability to computationally design these steels in high-throughput approaches lags behind other classes of alloys. In this article, we will provide a substantial review of the role of magnetism, magnetic excitations and transformations for alloy thermodynamics, point defects, interfaces and kinetics. This will be achieved by combining insights from different methods: Ab initio simulations have the advantage that the magnetic ground state is intrinsic part of the electronic minimization. Due to the coarsening of the many-electron structures and therewith magnetic interactions, tight-binding methods can handle larger system sizes. Effective interaction models provide the freedom to exploit more sophisticated magnetic interactions. The performance of these methods in terms of magnetic properties of Fe alloys will be evaluated by providing state-of-the-art results for their sensitivity to magnetism. Furthermore, dedicated experiments will be discussed to complete the understanding of magnetic effects in Fe alloys and to validate the modeling strategy. KW - Magnetic excitations KW - Defects KW - Diffusion KW - Density functional theory KW - Tight-binding KW - Effective interaction models PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-634530 DO - https://doi.org/10.1515/ijmr-2023-0225 SN - 1862-5282 VL - 60 IS - 99 SP - 1 EP - 14761 PB - Walter de Gruyter GmbH AN - OPUS4-63453 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Anker, Andy S. A1 - Aspuru-Guzik, Alán A1 - Ben Mahmoud, Chiheb A1 - Bennett, Sophie A1 - Briling, Ksenia R. A1 - Changiarath, Arya A1 - Chong, Sanggyu A1 - Collins, Christopher M. A1 - Cooper, Andrew I. A1 - Crusius, Daniel A1 - Darmawan, Kevion K. A1 - Das, Basita A1 - David, Nicholas A1 - Day, Graeme M. A1 - Deringer, Volker L. A1 - Duarte, Fernanda A1 - Eardley-Brunt, Annabel A1 - Evans, Matthew L. A1 - Evans, Rob A1 - Fairlamb, Ian A1 - Franklin, Barnabas A. A1 - Frey, Jeremy A1 - Ganose, Alex M. A1 - Goulding, Mark A1 - Hafizi, Roohollah A1 - Hakkennes, Matthijs A1 - Hickey, Niamh A1 - James, Gillian A1 - Jelfs, Kim E. A1 - Kalikadien, Adarsh V. A1 - Kapil, Venkat A1 - Koczor-Benda, Zsuzsanna A1 - Krammer, Ferdinand A1 - Kulik, Heather J. A1 - Kumar, Vishank A1 - Kuttner, Christian A1 - Lam, Erwin A1 - Lou, Yuchen A1 - Mante, Eltjo A1 - Martin, Jennie A1 - Mroz, Austin M. A1 - Nematiaram, Tahereh A1 - Pare, Charles W. P. A1 - Patra, Sarbani A1 - Proudfoot, James A1 - Ruscic, Branko A1 - Ryder, Matthew R. A1 - Sakaushi, Ken A1 - Saßmannshausen, Jörg A1 - Savoie, Brett M. A1 - Schneider, Nadine A1 - Schwaller, Philippe A1 - Skjelstad, Bastian Bjerkem A1 - Sun, Wenhao A1 - Szczypiński, Filip T. A1 - Torrisi, Steven A1 - Ueltzen, Katharina A1 - Vishnoi, Shubham A1 - Walsh, Aron A1 - Wang, Xinwei A1 - Wilson, Chloe A1 - Wu, Ruiqi A1 - Zeitler, Jakob T1 - Discovering structure–property correlations: General discussion N2 - This article is a discussion of the paper "Web-BO: Towards increased accessibility of Bayesian optimisation (BO) for chemistry" by Austin M. Mroz, Piotr N. Toka, Ehecatl Antonio del Río Chanona and Kim E. Jelfs (Faraday discussions, 2025, 256, 221-234). KW - Materials design KW - Machine learning KW - Automation KW - Materials discovery PY - 2025 DO - https://doi.org/10.1039/d4fd90062f SN - 1359-6640 SN - 1364-5498 VL - 256 IS - Themed collection: Data-driven discovery in the chemical sciences SP - 373 EP - 412 PB - Royal Society of Chemistry (RSC) CY - Cambridge AN - OPUS4-62208 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -