Active particles can form two-dimensional solids with long-range crystalline order and giant spontaneous deformations, which differ from those formed by nonmotile particles. These active systems exhibit quasi-long-range positional order and true long-range orientational order, similar to equilibrium solids. The power-law exponents describing the positional order in active systems cover a wide range, reaching values as high as 20. Understanding the interplay between order and fluctuations in active solids is crucial for integrating active elements into materials and fabrication processes.
A recent study challenges the conventional definition of crystals by revealing that crystal structures are not always regularly arranged. The discovery of the random stacking of hexagonal layers (RHCP) as a stable structure overturns previous beliefs and has implications for materials science, particularly in semiconductors, solar panels, and electric vehicle technologies. The study provides insights into polytypism and suggests that polytypic materials like silicon carbide may have continuous structural transitions with new useful properties. The research was conducted using X-ray scattering data and advanced computation techniques.
Recent advancements in structural feature representations and generative neural networks have the potential to efficiently predict stable crystal structures, enabling the design of solid-state crystalline materials with desired properties. Crystal structure prediction (CSP) plays a crucial role in discovering stable and metastable structures for materials of unknown structure. Efficient optimization techniques, such as evolutionary algorithms and particle swarm optimization, have led to the discovery of various new materials. The use of generative adversarial networks and Euclidean neural networks shows promise in learning and discovering crystallographic structures.
Scientists have used high-pressure modeling to predict four new compounds made up of lithium and cesium, which have crystal structures that have never been seen before and can act as superconductors. The compounds were predicted using a crystal structure prediction algorithm called USPEX, which analyzed how high pressure might affect electronegativity. The discovery opens up new areas for scientists to explore and could lead to the development of materials useful for ultra-fast microchips and ultra-efficient power grid materials.
Researchers at Cornell University have discovered over 20 new self-assembled crystal structures using a targeted computational approach. The team developed a new functional form for particle interactions, allowing them to control various features of the particles' interaction landscape. The findings suggest that there are potentially limitless new and exotic materials configurations possible through controlled self-assembly, serving as design targets for researchers who make nanoparticles and colloids.
Researchers at the National Institute of Standards and Technology (NIST) and KTH Royal Institute of Technology in Sweden have used high-speed X-ray diffraction to identify the crystal structures that form within steel as it is 3D-printed. The findings unlock a computational tool for 3D-printing professionals, offering them a greater ability to predict and control the characteristics of printed parts, potentially improving the technology's consistency and feasibility for large-scale manufacturing.
