Elastic ceramics: Aerogels hold promise as lightweight replacements for thermal insulation. However, poor mechanical stability has hampered progress in moving toward commercialization. Xu et al. designed a mechanical metamaterial that pinches in a small amount when you compress it (see the Perspective by Chhowalla and Jariwala). This is characteristic of materials with a negative Poisson's ratio and dramatically improves mechanical stability. The trick was using three-dimensional graphene structures to template the ceramic aerogels, thus producing a superinsulating material endowed with excellent mechanical properties. Science , this issue p. 723 ; see also p. 694

The sol–gel method is an attractive technique to synthesize homogeneous silicate glasses with high purity while relying on a lower synthesis temperature than in the melt-quench method. However, the mechanism and kinetics of the condensation of the silicate network in aqueous solution remain unclear. Here, based on reactive molecular dynamics simulations (ReaxFF), we investigate the sol–gel condensation kinetics of a silica glass. The influence of the potential parametrization and system size is assessed. Our simulation methodology is found to offer good agreement with experiments. We show that the aqueous concentration of the Si(OH)₄ precursors and the local degree of polymerization of the Si atoms play a crucial role in controlling the condensation activation energy. Based on our simulations, we demonstrate that the gelation reaction is driven by the existence of some local atomic stress that gets released upon condensation.

Enhancing the ion conduction in solid electrolytes is critically important for the development of high-performance all-solid-state lithium-ion batteries (LIBs). Lithium thiophosphates are among the most promising solid electrolytes, as they exhibit superionic conductivity at room temperature. However, the lack of comprehensive understanding of their ion conduction mechanism, especially the effect of structural disorder on ionic conductivity, is a long-standing problem that limits further innovations in all-solid-state LIBs. Here, we address this challenge by establishing and employing a deep learning potential to simulate Li₃PS₄ electrolyte systems with varying levels of disorder. The results show that disorder-driven diffusion dynamics significantly enhances the room-temperature conductivity. We further establish bridges between dynamical characteristics, local structural features, and atomic rearrangements by applying a machine learning-based structure fingerprint termed “softness”. This metric allows the classification of the disorder-induced “soft” hopping lithium ions. Our findings offer insights into ion conduction mechanisms in complex disordered structures, thereby contributing to the development of superior solid-state electrolytes for LIBs.