Computational diffraction reveals long‐range strains, distortions and disorder in molecular dynamics simulations of irradiated single crystals. Issue 2 (11th March 2022)

• Record Type: Journal Article
• Authors: Boulle, A.; Chartier, A.; Debelle, A.; Jin, X.; Crocombette, J.-P.
• Is Part Of: Journal of applied crystallography. Volume 55:Issue 2(2022)
• Page Start: 296
• Page End: 309
• Publication Date: 2022-03-11
• Subjects: molecular dynamics -- X‐ray diffraction -- defects -- disorder; Crystallography -- Periodicals; 548.05
• DOI: 10.1107/S1600576722001406
• Languages: English
• ISSNs: 0021-8898

Abstract

Abstract : Reciprocal space maps are numerically generated from molecular dynamics simulation data. This approach, referred to as computational diffraction, allows the determination of long‐rage strains, distortions and disorder. Abstract : Atomic‐scale simulations, and in particular molecular dynamics (MD), are key assets to model the behavior of the structure of materials under the action of external stimuli, say temperature, strain or stress, irradiation, etc . Despite the widespread use of MD in condensed matter science, some basic material characteristics remain difficult to determine. This is, for instance, the case for the long‐range strain tensor, and its root‐mean‐squared fluctuations, in disordered materials. In this work, computational diffraction is introduced as a fast and reliable structural characterization tool of atomic‐scale simulation cells in the case of irradiated single crystals. In contrast to direct‐space methods, computational diffraction operates in the reciprocal space and is therefore highly sensitive to long‐range spatial correlations. With the example of irradiated UO2 single crystals, it is demonstrated that the normal strains, shear strains and rotations, as well as their root‐mean‐squared fluctuations (microstrain) and the atomic disorder, are straightforwardly and unambiguously determined. The methodology presented here has been developed with efficiency in mind, in order to be able to provide simple and reliable characterizations either operating in real time, in parallel with other analysis tools, or operating on very large data sets.