Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
Structure of solids and liquids is the study of the physical properties of matter in which there is a non-negligible interaction between the constituent atoms or molecules. While the atoms or molecules in a liquid are free to move around, those in solids are limited to vibrating about a fixed point.
Using water as a medium for dynamic modulation of competitive plasticity and viscoelasticity provides a unique perspective to attain adaptive materials compared to conventional passive polymer processing. Here the authors report cellulose phenoxyacetate as a sustainable polymer with unusual water- responsive dual mechanic functionalities.
Aperiodic crystals, including modulated structures and quasicrystals, are fascinating, but their relationship to each other is enigmatic. Here, the authors reveal the pivotal role of aperiodic approximants that unify those crystals under the concept of superspace.
The shape and trajectory of a crack plays a crucial role in material fracture. High-precision experiments now directly capture this phenomenon, unveiling the intricate 3D nature of cracks.
The atomic reconstruction and stacking arrangement in twisted trilayer graphene with a range of varying twist angles are elucidated by four-dimensional scanning transmission electron microscopy, revealing the hierarchical moiré of moiré superstructures that govern the structural symmetry at different length scales.
Ageing is a non-linear, irreversible process that defines many properties of glassy materials. Now, it is shown that the so-called material-time formalism can describe ageing in terms of equilibrium-like properties.
Two-dimensional crystals have revolutionized fundamental research across a staggering range of disciplines. We take stock of the progress gained after twenty years of work.
Machine learning interatomic potentials (MLIPs) enable materials simulations at extended length and time scales with near-ab initio accuracy. They have broad applications in the study and design of materials. Here, we discuss recent advances, challenges, and the outlook for MLIPs.