In the search for happiness, having a good day every day is surely crucial. But when there are so many pursuits competing for our attention, sometimes it's difficult to know how much time to allocate for each one. Now, scientists in Canada claim to cracked the code for the perfect day – and surprisingly, it includes a short shift at work. According to the experts, the formula for the perfect day is six hours of family time, two hours spent with friends, 1.5 hour socialising, two hours exercising and one hour eating and drinking. Additionally, the perfect day should involve no more than six hours of work and less than 15 minutes commuting.

The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resource-demanding process. To accelerate the design of novel metamaterials, we present MetaScientist, a human-in-the-loop system that integrates advanced AI capabilities with expert oversight with two primary phases: (1) hypothesis generation, where the system performs complex reasoning to generate novel and scientifically sound hypotheses, supported with domain-specific foundation models and inductive biases retrieved from existing literature; (2) 3D structure synthesis, where a 3D structure is synthesized with a novel 3D diffusion model based on the textual hypothesis and refined it with a LLM-based refinement model to achieve better structure properties. At each phase, domain experts iteratively validate the system outputs, and provide feedback and supplementary materials to ensure the alignment of the outputs with scientific principles and human preferences. Through extensive evaluation from human scientists, MetaScientist is able to deliver novel and valid mechanical metamaterial designs that have the potential to be highly impactful in the metamaterial field.

If you've always wondered what it takes to be a badass, a new study reveals the strict criteria. Following questionnaires involving over 2,000 people, researchers in the US have officially improved on the dictionary definition of the term. A badass has an'outer toughness' (consisting of physical strength, a'formidable presence', or both), an inner toughness (such as moral resilience and courage), or both. That's why'radically' different men and women – ranging from peace advocates to fierce warriors – can be considered badasses, according to the experts. Famous badasses include Genghis Khan (AD 1162 to 1227), the brutal founder of the Mongol Empire responsible for the deaths of around 40 million people, they say.

The conflict between stiffness and toughness is a fundamental problem in engineering materials design. However, the systematic discovery of microstructured composites with optimal stiffness-toughness trade-offs has never been demonstrated, hindered by the discrepancies between simulation and reality and the lack of data-efficient exploration of the entire Pareto front. We introduce a generalizable pipeline that integrates physical experiments, numerical simulations, and artificial neural networks to address both challenges. Without any prescribed expert knowledge of material design, our approach implements a nested-loop proposal-validation workflow to bridge the simulation-to-reality gap and discover microstructured composites that are stiff and tough with high sample efficiency. Further analysis of Pareto-optimal designs allows us to automatically identify existing toughness enhancement mechanisms, which were previously discovered through trial-and-error or biomimicry. On a broader scale, our method provides a blueprint for computational design in various research areas beyond solid mechanics, such as polymer chemistry, fluid dynamics, meteorology, and robotics.

In this paper, we develop a convolutional neural network model to predict the mechanical properties of a two-dimensional checkerboard composite quantitatively. The checkerboard composite possesses two phases, one phase is soft and ductile while the other is stiff and brittle. The ground-truth data used in the training process are obtained from finite element analyses under the assumption of plane stress. Monte Carlo simulations and central limit theorem are used to find the size of the dataset needed. Once the training process is completed, the developed model is validated using data unseen during training. The developed neural network model captures the stiffness, strength, and toughness of checkerboard composites with high accuracy. Also, we integrate the developed model with a genetic algorithm (GA) optimizer to identify the optimal microstructural designs. The genetic algorithm optimizer adopted here has several operators, selection, crossover, mutation, and elitism. The optimizer converges to configurations with highly enhanced properties. For the case of the modulus and starting from randomly-initialized generation, the GA optimizer converges to the global maximum which involves no soft elements. Also, the GA optimizers, when used to maximize strength and toughness, tend towards having soft elements in the region next to the crack tip.

Scientists have developed a fibre that combines the elasticity of rubber with the strength of a metal. Researchers at North Carolina State University are behind the innovation, which has created a tougher material that could be incorporated into soft robotics, packaging materials or next-generation textiles. The team made fibres consisting of a gallium metal core surrounded by an elastic polymer sheath. When placed under stress, the fibre has the strength of the metal core. But whereas the metal eventually breaks, the fiber doesn't fail - the polymer sheath absorbs the strain between the breaks in the metal and transfers the stress back to the metal core.