ترغب بنشر مسار تعليمي؟ اضغط هنا

Computational diffraction reveals long-range strains, disorder and crystalline domains in atomic scale simulations

62   0   0.0 ( 0 )
 نشر من قبل Alexandre Boulle
 تاريخ النشر 2021
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

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.



قيم البحث

اقرأ أيضاً

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 inding it ranges from 674 ${pm}$ 56 W/m/K at 100 K to 186 ${pm}$ 7 W/m/K at 400 K, with a value of 237 ${pm}$ 6 W/m/K at room temperature. We compare these data with analytical models and first principles calculations, taking into account atomic-scale defects (O, Si, C impurities, and Al vacancies). We find Al vacancies play the greatest role in reducing thermal conductivity because of the largest mass-difference scattering. Modeling also reveals that 10% of heat conduction is contributed by phonons with long mean free paths, over ~7 ${mu}$m at room temperature, and 50% by phonons with MFPs over ~0.3 ${mu}$m. Consequently, the effective thermal conductivity of AlN is strongly reduced in sub-micron thin films or devices due to phonon-boundary scattering.
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 onfigurations within finite atom-centered environments. The obvious downside of this approach is that it cannot capture non-local, non-additive effects such as those arising due to long-range electrostatics or quantum interference. We propose a solution to this problem by introducing non-local representations of the system that are remapped as feature vectors that are defined locally and are equivariant in O(3). We consider in particular one form that has the same asymptotic behavior as the electrostatic potential. We demonstrate that this framework can capture non-local, long-range physics by building a model for the electrostatic energy of randomly distributed point-charges, for the unrelaxed binding curves of charged organic molecular dimers, and for the electronic dielectric response of liquid water. By combining a representation of the system that is sensitive to long-range correlations with the transferability of an atom-centered additive model, this method outperforms current state-of-the-art machine-learning schemes, and provides a conceptual framework to incorporate non-local physics into atomistic machine learning.
219 - Binghui Ge , Jing Zhu 2011
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 rengthening capacities in high temperature. By means of high resolution scanning transmission electron microscopy (HRSTEM) and 3D atom probe (3DAP) tomography, Srinivasan et al. proposed a new point that in nickel base superalloys there exist two different interfacial widths across the {gamma}/{gamma} interface, one corresponding to an order-disorder transition, and the other to the composition transition. We argue about this conclusion in this comment.
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 tions, charge states, orbital occupancies, or magnetic structure. Disorder can sometimes be random, but more usually it is correlated. Frontier research into disordered crystals now seeks to control and exploit the unusual patterns that persist within these correlated disordered states in order to access functional responses inaccessible to conventional crystals. In this review we survey the core design principles at the disposal of materials chemists that allow targeted control over correlated disorder. We show how these principles---often informed by long-studied statistical mechanical models---can be applied across an unexpectedly broad range of materials, including organics, supramolecular assemblies, oxide ceramics, and metal--organic frameworks. We conclude with a forward-looking discussion of the exciting link to function in responsive media, thermoelectrics, topological phases, and information storage.
Tungsten is the main candidate material for plasma-facing armour components in future fusion reactors. Bombardment with energetic fusion neutrons causes collision cascade damage and defect formation. Interaction of defects with helium, produced by tr ansmutation and injected from the plasma, modifies defect retention and behaviour. Here we investigate the residual lattice strains caused by different doses of helium-ion-implantation into tungsten and tungsten-rhenium alloys. Energy and depth-resolved synchrotron X-ray micro-diffraction uniquely permits the measurement of lattice strain with sub-micron 3D spatial resolution and ~10-4 strain sensitivity. Increase of helium dose from 300 appm to 3000 appm increases volumetric strain by only ~2.4 times, indicating that defect retention per injected helium atom is ~3 times higher at low helium doses. This suggests that defect retention is not a simple function of implanted helium dose, but strongly depends on material composition and presence of impurities. Conversely, analysis of W-1wt% Re alloy samples and of different crystal orientations shows that both the presence of rhenium, and crystal orientation, have comparatively small effect on defect retention. These insights are key for the design of armour components in future reactors where it will be essential to account for irradiation-induced dimensional change when predicting component lifetime and performance.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا