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Advanced experimental and numerical approaches are being developed to
capture the localization of plasticity at the nanometer scale as a function of the multiscale and heterogeneous microstructure present in metallic materials.
These innovative approaches promise new avenues to understand microstructural effects on mechanical properties, accelerate alloy design, and enable more accurate mechanical property prediction. This article provides an overview of emerging approaches with a focus on the localization of plasticity by crystallographic slip. New insights into the mechanisms and mechanics of strain localization are addressed. The consequences of the localization of plasticity by deformation slip for mechanical properties of metallic materials are also detailed.
An in-depth understanding of the degradation mechanisms is a prerequisite for developing the nextgeneration all solid-state lithium metal battery (ASSLMB) technology. Herein, synchrotron X-ray computed tomography (SXCT) together with other probing tools and simulation method were employed to rediscover the decaying mechanisms of LiNi0.8Co0.1Mn0.1O2 (NCM)|Li6PS5Cl (LPSCl)|Li ASSLMB. It reveals that the detachment and isolation of NCM particles cause the current focusing on the remaining active regions of cathode. The extent of Li stripping and the likelihood of Li+ plating into LPSCl facing the active NCM particles becomes higher. Besides, the homogeneity of Li stripping/plating is improved by homogenizing the electrochemical reactions at the cathode side by LiZr2(PO4)3 (LZP) coating. These results suggest a codependent failure mechanism between cathode and anode that is mediated by uneven Li ion flux. This work contributes to establish a holistic understanding of the degradation mechanisms in ASSLMBs and opens new opportunities for their further optimization and evelopment.
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.