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This is a case study produced by Rita Saade, Protection Advisor at Oxfam in Lebanon, and Lara AlBekai, Protection Officer at Oxfam in Lebanon, as part of the Resource Pack on Community-Based Protection. The document discusses capacity strengthening, material support, self-protection outside community protection structures and social cohesion.

Dopants can significantly affect the properties of oxide ceramics through their impact on the property-determined microstructure characteristics such as grain boundary segregation, space charge layer formation in the grain boundary vicinity, and the resultant microstructure features like bimodality due to abnormal grain growth. To support rational oxide ceramics design, we propose a multiphysics-based and defect-chemistry-informed phase-field grain growth model to simulate the microstructure evolution of oxide ceramics. It fully respects the thermodynamics of charged point defects (oxygen vacancies and dopants) in both the grain interior and boundaries and considers the competing kinetics of defect diffusion and grain boundary movement. The proposed phase-field model is benchmarked against well-known simplified bicrystal models, including the Mott-Schottky and Gouy-Chapman models. Various simulation results are presented to reveal the impacts of defect formation energy differences between the grain interior and the grain boundary core on the key microstructural aspects. In particular, simulation results confirm that the solute drag effect alone can lead to bimodal grain size distribution, without any contribution from grain misorientation and other anisotropy. Interestingly, abnormal grain growth simulations demonstrate that grain boundary potentials can vary substantially: grain boundaries of larger grains tend to have lower potentials than those of smaller grains. Such heterogeneous grain boundary potential distribution may inspire a new material optimization strategy through microstructure design. This study provides a comprehensive framework for defect-chemistry-consistent investigations of microstructure evolution in polycrystalline oxide ceramics, offering fundamental insights into in-situ processes during critical manufacturing stages.

Characteristic shock effects in silica serve as a key indicator of historical impacts at geological sites. Despite this geological significance, atomistic details of structural transformations under high pressure and shock compression remain poorly understood. This ambiguity is evidenced by conflicting experimental observations of both amorphization and crystallization transitions. Utilizing a newly developed machine-learning interatomic potential, we examine the response of $\alpha$-quartz to shock compression with a peak pressure of 60 GPa over nano-second timescales. We initially observe amorphization before recrystallization into a d-NiAs-structured silica with disorder on the silicon sublattice, accompanied by the formation of domains with partial order of silicon. Investigating a variety of strain conditions enables us to identify the non-hydrostatic stress and strain states that allow the direct diffusionless formation of rosiaite-structured silica.

Efficient, reliable and easy-to-use structure recognition of atomic environments is essential for the analysis of atomic scale computer simulations. In this work, we train two neuronal network (NN) architectures, namely PointNet and dynamic graph convolutional NN (DG-CNN) using different hyperparameters and training regimes to assess their performance in structure identification tasks of atomistic structure data. We show benchmarks on simple crystal structures, where we can compare against established methods. The approach is subsequently extended to structurally more complex SiO$_2$ phases. By making use of this structure recognition tool, we are able to achieve a deeper understanding of the crystallization process in amorphous SiO$_2$ under shock compression. Lastly, we show how the NN based structure identification workflows can be integrated into OVITO using its python interface.

Silicon oxycarbides show outstanding versatility due to their highly tunable composition and microstructure. Consequently, a key challenge is a thorough knowledge of structure-property relations in the system. In this work, we fit an atomic cluster expansion potential to a set of actively learned DFT training data spanning a wide configurational space. We demonstrate the ability of the potential to produce realistic amorphous structures and rationalize the formation of different morphologies of the turbostratic free carbon phase. Finally, we relate the materials stiffness to its composition and microstructure, finding a delicate dependence on Si-C bonds that contradicts commonly assumed relations to the free carbon phase.

We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry.

Silicon-oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si-O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si-O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning.

Designing the microstructure of Fe-Ni permalloy by additive manufacturing (AM) opens new avenues to tailor the materials' magnetic properties. Yet, AM-produced parts suffer from spatially inhomogeneous thermal-mechanical and magnetic responses, which are less investigated in terms of process simulation and modeling schemes. Here we present a powder-resolved multiphysics-multiscale simulation scheme for describing magnetic hysteresis in materials produced via AM. The underlying physical processes are explicitly considered, including the coupled thermal-structural evolution, chemical order-disorder transitions, and associated thermo-elasto-plastic behaviors. The residual stress is identified as the key thread in connecting the physical processes and in-process phenomena across scales. By employing this scheme, we investigate the dependence of the fusion zone size, the residual stress and plastic strain, and the magnetic hysteresis of AM-produced Fe21.5Ni78.5 permalloy on beam power and scan speed. Simulation results also suggest a phenomenological relation between magnetic coercivity and average residual stress, which can guide the magnetic hysteresis design of soft magnetic materials by choosing appropriate AM-process parameters.

Nanoporous metals or nanofoams are a promising material class that is considered for sensing, actuation, and catalysis. To date, they mostly based on simple noble metals such as nanoporous gold, which exhibit peculiar stress-strain response different from the bulk material. At the same time bulk alloys such as NiTi feature a reversible martensitic phase transition giving rise to interesting shape memory and superelastic effects. Combining the rich mechanics of NiTi with the geometrical features of a nanofoam is expected to improve the mechanical performance of this material. In this atomistic study we explore the behavior of a NiTi nanofoam at varying temperature and its reaction to (cyclic) compression. Using molecular dynamics simulations we track the microscopic processes enabling reversible deformation as well as the mechanical failure mechanisms of the NiTi nanofoam.

The nature of mechanically induced dislocations in SrTiO3 at low temperatures has been a disputed matter for a long time. Here we provide a systematic overview of the existing knowledge on dislocations in stoichiometric SrTiO3 complemented by computational models of several of the proposed dislocation types. Based on atomistic simulations we reveal their structure and mobility and put our focus on the splitting into partial dislocations, because this mechanism is held responsible for the good dislocation mobility in strontium titanate. Our results reveal that dislocations with a {1-10} glide plane (types A, B, and C) show easy glide behavior due to their glide dissociated configuration. The motion of dislocations on {001} glide planes (type E), however, is prohibited due to high Peierls barriers, as is the case for the {1-1-2} glide plane (type D). For dislocations with (partial) edge character the dissociation into partials is shown to be very sensitive to the charge state of the dislocation core. Since, in turn, dislocation mobility is strongly related to the dissociation of dislocations, the macroscopic ductility may in some cases have a direct relation to the charge at the dislocation core.

Ultra-dense domain walls are increasingly important for many devices but their microscopic properties are so far not fully understood. In this study we combine atomistic and coarse-grained molecular dynamic simulations to study the domain wall stability in the prototypical ferroelectric BaTiO3. We transfer the discussion of the field-driven nucleation and motion of domain walls to thermally induced modifications of the wall without an external driving force. Our simulations show that domain wall dynamics and stability depend crucially on microscopic thermal fluctuations. Enhanced fluctuations at domain walls may result in the formation of critical nuclei for the permanent shift of the domain wall. If two domain walls are close - put in other words, when domains are small - thermal fluctuations can be sufficient to bring domain walls into contact and lead to the annihilation of small domains. This is even true well below the Curie temperature and when domain walls are initially as far apart as 6 unit cells. Such small domains are, thus, not stable and limit the maximum achievable domain wall density in nanoelectronic devices.