English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Simulating short-range order in compositionally complex materials

MPS-Authors
/persons/resource/persons125232

Körmann,  Fritz
Department of Materials Science and Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands;
Complex Concentrated Alloys, Project Groups, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

/persons/resource/persons125293

Neugebauer,  Jörg
Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Ferrari, A., Körmann, F., Asta, M. D., & Neugebauer, J. (2023). Simulating short-range order in compositionally complex materials. Nature Computational Science, 3(3), 221-229. doi:10.1038/s43588-023-00407-4.


Cite as: https://hdl.handle.net/21.11116/0000-000D-5A28-2
Abstract
In multicomponent materials, short-range order (SRO) is the development of correlated arrangements of atoms at the nanometer scale. Its impact in compositionally complex materials has stimulated an intense debate within the materials science community. Understanding SRO is critical to control the properties of technologically relevant materials, from metallic alloys to functional ceramics. In contrast to long-range order, quantitative characterization of the nature and spatial extent of SRO evades most of the experimentally available techniques. Simulations at the atomistic scale have full access to SRO but face the challenge of accurately sampling high-dimensional configuration spaces to identify the thermodynamic and kinetic conditions at which SRO is formed and what impact it has on material properties. Here we highlight recent progress in computational approaches, such as machine learning-based interatomic potentials, for quantifying and understanding SRO in compositionally complex materials. We briefly recap the key theoretical concepts and methods.