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Corrosion evaluation of Al-Cu-Mn-Zr cast alloys in 3.5% NaCl solution
Corrosion behavior of cast Al-Cu-Mn-Zr (ACMZ) and RR350 alloys was compared to a cast 319 alloy in 3.5 wt.% NaCl. After 168 h immersion, ACMZ and RR350 alloys suffered from preferential attack adjacent to intermetallic particles decorated at grain boundaries while the attack in 319 occurred in eutectic Al-Si dendritic boundaries. Electrochemical data allowed semiquantitative comparison of alloy resistance to corrosion initiation, and ACMZ type alloys, including RR350 and three alloys with higher Cu, were considered more resistant than 319 due to the absence of deleterious Si particles. In case of 319, such Si particles presumably drove higher micro-galvanic influence to initiate and sustain Al corrosion. With lower susceptibility to corrosion initiation, ACMZ alloys should exhibit higher or at minimum similar resistance compared to cast 319.
Curiosity shapes spatial exploration and cognitive map formation in humans
Cognitive maps are thought to arise, at least in part, from our intrinsic curiosity to explore unknown places. However, it remains untested how curiosity shapes aspects of spatial exploration in humans. Combining a virtual reality task with indices of exploration complexity, we found that pre-exploration curiosity states predicted how much individuals spatially explored environments, whereas markers of visual exploration determined post-exploration feelings of interest. Moreover, individual differences in curiosity traits, particularly Stress Tolerance, modulated the relationship between curiosity and spatial exploration, suggesting the capacity to cope with uncertainty enhances the curiosity-exploration link. Furthermore, both curiosity and spatial exploration predicted how precisely participants could recall spatial-relational details of the environment, as measured by a sketch map task. These results provide new evidence for a link between curiosity and exploratory behaviour, and how curiosity might shape cognitive map formation.
Recent advances in high-entropy superconductors
High-entropy materials (HEMs) exhibit significant potential for diverse applications owing to their tunable properties, which can be precisely engineered through the selection of specific elements and the modification of stoichiometric ratios. The discovery of superconductivity in HEMs has garnered considerable interest, leading to accelerated advancements in this field in recent years. This review provides an overview of various high-entropy superconductors, highlighting their distinct features, such as disordered crystal structure, factors affecting the critical temperature (Tc), unconventional superconductivity, and topological bands. A perspective on this field is subsequently proposed, drawing upon insights from recently published academic literature. The objective is to provide researchers with a comprehensive and clear understanding of the newly developed high-entropy superconductivity, thereby catalyzing further advancements in this domain.
Constructing multicomponent cluster expansions with machine-learning and chemical embedding
Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increase in the number of fitting parameters and training set size. We introduce the embedded cluster expansion (eCE) formalism that enables the parameterization of accurate on-lattice surrogate models for alloys containing several chemical species. The eCE model simultaneously learns a low dimensional embedding of site basis functions along with the weights of an energy model. A prototypical senary alloy comprised of elements in groups 5 and 6 of the periodic table is used to demonstrate that eCE models can accurately reproduce ordering energetics of complex alloys without a significant increase in model complexity. Further, eCE models can leverage similarities between chemical elements to efficiently extrapolate into compositional spaces that are not explicitly included in the training dataset. The eCE formalism presented in this study unlocks the possibility of employing cluster expansion models to study multicomponent alloys containing several alloying elements.
A scalable synergy-first backbone decomposition of higher-order structures in complex systems
In the last decade, there has been an explosion of interest in the field of multivariate information theory and the study of emergent, higher-order interactions. These “synergistic” dependencies reflect information that is in the “whole” but not any of the “parts.” Arguably the most successful framework for exploring synergies is the partial information decomposition (PID). Despite its considerable power, the PID has a number of limitations that restrict its general applicability. Subsequently, other heuristic measures, such as the O-information, have been introduced, although these measures typically only provide a summary statistic of redundancy/synergy dominance, rather than direct insight into the synergy itself. To address this issue, we present an alternative decomposition that is synergy-first, scales much more gracefully than the PID, and has a straightforward interpretation. We define synergy as that information encoded in the joint state of a set of elements that would be lost following the minimally invasive perturbation on any single element. By generalizing this idea to sets of elements, we construct a totally ordered “backbone” of partial synergy atoms that sweeps the system’s scale. This approach applies to the entropy, the Kullback-Leibler divergence, and by extension, to the total correlation and the single-target mutual information (thus recovering a “backbone” PID). Finally, we show that this approach can be used to decompose higher-order interactions beyond information theory by showing how synergistic combinations of edges in a graph support global integration via communicability. We conclude by discussing how this perspective on synergistic structure can deepen our understanding of part-whole relationships in complex systems.
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