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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.
Quasi-liquid layers on the surface of water ice significantly affect its distinctive chemical and physical properties, but a molecular-level understanding of these heterogeneous layers is missing. Here, the authors use molecular dynamics simulations and machine learning analyses to show that the quasi-liquid layers resemble supercooled water.
The structure of crystalline materials plays a central role in materials science, but the disordered structure of metallic glass is difficult to characterize and describe. Here, the authors use diffusion maps on atomistic data to obtain general structural descriptors tied to atomic positions.
Through neutron scattering experiments coupled with machine learning, the authors uncover the strong role of nuclear quantum effects in the dynamics of ammonia in both its solid and technologically relevant liquid phase.
The thermalization of acoustic phonons after photoexcitation is traced by electron pulses in TiSe2, and the excitonic contribution to the structural order parameter of the material’s charge density wave phase is quantified.
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.