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Atomic scale simulations are a key element of modern science in that they allow to understand, and even predict, complex physical or chemical phenomena on the basis of the fundamental laws of nature. Among the different existing atomic scale simulation approaches, molecular dynamics (MD) has imposed itself as the method of choice 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 of the long-range strain tensor in heavily disordered materials, or the quantification of rotated crystalline domains lacking clearly defined boundaries. In this work, we introduce computational diffraction as a fast and reliable structural characterization tool of atomic scale simulation cells. As compared to usual direct-space methods, computational diffraction operates in the reciprocal-space and is therefore highly sensitive to long-range spatial correlations. With the example of defective UO2, it is demonstrated that the homogeneous strain tensor, the heterogeneous strain tensor, the disorder, as well as rotated crystallites are straightforwardly and unambiguously determined. Computational diffraction can be applied to any type of atomic scale simulation and can be performed in real time, in parallel with other analysis tools. In experimental workflows, diffraction and microscopy are almost systematically used together in order to benefit from their complementarity. Computational diffraction, used together with computational microscopy, can potentially play a major role in the future of atomic scale simulations.
Aluminum nitride (AlN) plays a key role in modern power electronics and deep-ultraviolet photonics, where an understanding of its thermal properties is essential. Here we measure the thermal conductivity of crystalline AlN by the 3${omega}$ method, f
The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a bulk material are written as a sum over contributions that depend on the c
Interfaces have long been known to be the key to many mechanical and electric properties. To nickel base superalloys which have perfect creep and fatigue properties and have been widely used as materials of turbine blades, interfaces determine the st
Crystals are a state of matter characterised by periodic order. Yet crystalline materials can harbour disorder in many guises, such as non-repeating variations in composition, atom displacements, bonding arrangements, molecular orientations, conforma
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