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Crystal plasticity is mediated through dislocations, which form knotted configurations in a complex energy landscape. Once they disentangle and move, they may also be impeded by permanent obstacles with finite energy barriers or frustrating long-range interactions. The outcome of such complexity is the emergence of dislocation avalanches as the basic mechanism of plastic flow in solids at the nanoscale. While the deformation behavior of bulk materials appears smooth, a predictive model should clearly be based upon the character of these dislocation avalanches and their associated strain bursts. We provide here a comprehensive overview of experimental observations, theoretical models and computational approaches that have been developed to unravel the multiple aspects of dislocation avalanche physics and the phenomena leading to strain bursts in crystal plasticity.
Room temperature ferromagnetism was characterized for thin films of SrTi$_{0.6}$Fe$_{0.4}$O$_{3-{delta}}$ grown by pulsed laser deposition on SrTiO$_{3}$ and Si substrates under different oxygen pressures and after annealing under oxygen and vacuum c
Crack initiation emerges due to a combination of elasticity, plasticity, and disorder, and it is heavily dependent on the materials microstructural details. In this paper, we investigate brittle metals with coarse-grained, microstructural disorder th
The simultaneous interplay of strong electron-electron correlations, topological zero-energy states, and disorder is yet an unexplored territory but of immense interest due to their inevitable presence in many materials. Copper oxide high-temperature
Finding new ionic conductors that enable significant advancements in the development of energy-storage devices is a challenging goal of current material science. Aside of material classes as ionic liquids or amorphous ion conductors, the so-called pl
Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here we develop and experimentally implement deep kernel learning workflow combining the correlative prediction of the t