Understanding the behavior and evolution of a dynamical many-body system by analyzing patterns in their experimentally captured images is a promising method relevant for a variety of living and non-living self-assembled systems. The arrays of moving liquid crystal skyrmions studied here are a representative example of hierarchically organized materials that exhibit complex spatiotemporal dynamics driven by multiscale processes. Joint geometric and topological data analysis (TDA) offers a powerful framework for investigating such systems by capturing the underlying structure of the data at multiple scales. In the TDA approach, we introduce the $Ψ$-function, a robust numerical topological descriptor related to both the spatiotemporal changes in the size and shape of individual topological solitons and the emergence of regions with their different spatial organization. The geometric method based on the analysis of vector fields generated from images of skyrmion ensembles offers insights into the nonlinear physical mechanisms of the system's response to external stimuli and provides a basis for comparison with theoretical predictions. The methodology presented here is very general and can provide a characterization of system behavior both at the level of individual pattern-forming agents and as a whole, allowing one to relate the results of image data analysis to processes occurring in a physical, chemical, or biological system in the real world.

Researchers at the University of Chicago Pritzker School of Molecular Engineering have demonstrated how to design the basic elements needed for logic operations with a material called liquid crystal. The new development is the first of its kind, and it could lead to a brand new way of performing computations.

Researchers with the University of Chicago Pritzker School of Molecular Engineering have shown for the first time how to design the basic elements needed for logic operations using a kind of material called a liquid crystal--paving the way for a completely novel way of performing computations. The results, published Feb. 23 in Science Advances, are not likely to become transistors or computers right away, but the technique could point the way towards devices with new functions in sensing, computing and robotics. "We showed you can create the elementary building blocks of a circuit--gates, amplifiers, and conductors--which means you should be able to assemble them into arrangements capable of performing more complex operations," said Juan de Pablo, the Liew Family Professor in Molecular Engineering and senior scientist at Argonne National Laboratory, and the senior corresponding author on the paper. The research aimed to take a closer look at a type of material called a liquid crystal. The molecules in a liquid crystal tend to be elongated, and when packed together they adopt a structure that has some order, like the straight rows of atoms in a diamond crystal--but instead of being stuck in place as in a solid, this structure can also shift around as a liquid does.

Scientists have developed an artificial version of cells in octopuses and squids that enable the marine creatures to match the colours and patterns of their surroundings, and disappear in an instant. Researchers from the University of Pennsylvania believe this may lead to novel camouflage applications in robotics, architecture and other fields such as cryptography and optics. Chromatophores are special cells in octopuses and squids that can expand and retract internal reflective plates in response to external stimuli and allow these molluscs to camouflage with the surroundings as well as to communicate signs of aggression or readiness to mate, researchers explained. In their new study, published in the journal Nature Materials, engineers used thin, flexible membranes – made from a polymer network of liquid crystals – to build an artificial chromatophore that can change colours instantly from near-infrared to visible to ultraviolet, on command. The membranes are situated over tiny cavities arranged in a grid, each of which can be inflated to a precise pressure, and as a cavity inflates, the membrane is stretched, shrinking its thickness and shifting its apparent colour, the study noted.

Machine learning algorithms have been available since the 1990s, but it is much more recently that they have come into use also in the physical sciences. While these algorithms have already proven to be useful in uncovering new properties of materials and in simplifying experimental protocols, their usage in liquid crystals research is still limited. This is surprising because optical imaging techniques are often applied in this line of research, and it is precisely with images that machine learning algorithms have achieved major breakthroughs in recent years. Here we use convolutional neural networks to probe several properties of liquid crystals directly from their optical images and without using manual feature engineering. By optimizing simple architectures, we find that convolutional neural networks can predict physical properties of liquid crystals with exceptional accuracy.

With Moore's Law slowing, engineers have been taking a cold hard look at what will keep computing going when it's gone. Certainly artificial intelligence will play a role. But there are stranger things in the computing universe, and some of them got an airing at the IEEE International Conference on Rebooting Computing in November. There were also some cool variations on classics such as reversible computing and neuromorphic chips. But some less-familiar ones got their time in the sun too, such as photonics chips that accelerate AI, nano-mechanical comb-shaped logic, and a "hyperdimensional" speech recognition system.