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Food choices shape both human and planetary health; yet, designing foods that are delicious, nutritious, and sustainable remains challenging. Here we show that generative artificial intelligence can learn the structure of the human palate directly from large-scale, human-generated recipe data to create novel foods within a structured design space. Using burgers as a model system, the generative AI rediscovers the classic Big Mac without explicit supervision and generates novel burgers optimized for deliciousness, sustainability, or nutrition. Compared to the Big Mac, its delicious burgers score the same or better in overall liking, flavor, and texture in a blinded sensory evaluation conducted in a restaurant setting with 101 participants; its mushroom burger achieves an environmental impact score more than an order of magnitude lower; and its bean burger attains nearly twice the nutritional score. Together, these results establish generative AI as a quantitative framework for learning human taste and navigating complex trade-offs in principled food design. 

Ultra-low-density elastomeric foams enable lightweight systems that combine high compliance with efficient energy return. Their mechanical response is inherently complex, characterized by high compressibility, nonlinear elasticity, and microstructural heterogeneity. In high-performance racing shoes, these foams are critical for low weight, high cushioning, and efficient energy return; yet, their constitutive behavior remains difficult to model and poorly understood. Here we integrate mechanical testing and machine learning to discover the mechanics of two ultra-low density elastomeric polymeric foams used in elite-level racing shoes. Across uniaxial tension, confined and unconfined compression, and simple shear, both foams exhibit pronounced tension–compression asymmetry, negligible lateral deformation consistent with an effective Poisson’s ratio close to zero, and low hysteresis indicative of an efficient energy return. Quantitatively, at a strain rate of 0.25/s, both foams provide a similar compressive stiffness (E = 268±16 kPa vs. E = 299±29 kPa), while one foam exhibits a 42% higher tensile stiffness (E = 884±69 kPa vs. E = 623±96 kPa), and nearly double the shear stiffness (G = 219±20 kPa vs. G = 117±24 kPa), implying a substantially greater lateral stiffness at a comparable vertical energy return (83.3±1.5% vs. 88.9±1.8%). By integrating these data into constitutive neural networks, paired with sparse regression, we discover compact, interpretable single-invariant models–supplemented by mixed-invariant or principal-stretch based terms–that capture the unique signature of the foams with$${\textsf {R}}^{\textsf {2}}$$values close to one across all loading modes. From a human performance perspective, these models have the potential to improve gait-level simulations with high-performance racing shoes to quantify running economy, performance enhancements, and injury risks on an individual athlete level. More broadly, this work establishes a scalable and interpretable approach for constitutive modeling of highly compressible, ultra-light elastomeric foams with applications to wearable technologies, soft robotics, and energy-efficient mobility systems. Source code, data, and examples are available at https://github.com/LivingMatterLab/CANN'>https://github.com/LivingMatterLab/CANN 

Global food production must reduce environmental impact while meeting rising demand for dietary protein. Plant-based meats aim to preserve the sensory and cultural role of animal meat while lowering greenhouse gas emissions, land use, and health risks. Advances in protein structure and flavor chemistry have improved product quality, yet consumers continue to prioritize taste and texture over sustainability, and systematic large-scale consumer surveys are scarce. It remains unclear how plant-based products rank against animal benchmarks and which product attributes most strongly influence overall liking. Here we show, in a large-scale blinded in-person sensory evaluation across 14 product categories, 2684 consumers, more than 11,000 product evaluations and 800,000 data points, that plant-based products still trail animal benchmarks at the category average level but approach parity in selected formats. Plant-based unbreaded chicken filets, chicken nuggets, and burgers achieved mean overall liking scores of 5.1, 4.9, and 5.2, differing from the animal benchmarks by only Δ = 0.1, 0.2, and 0.3 points on a seven-point scale. For unbreaded chicken filets and burgers, 48% and 47% of the participants rated the plant-based product the same as or better than the animal benchmark. Categories with higher sensory parity captured 5–14% market share compared with less than 1% for low-parity categories. Penalty analysis identified savoriness, aftertaste, juiciness, and tenderness as the strongest determinants of liking. These findings show that sensory parity is technically achievable but not yet consistent across product types. By publicly sharing all the sensory, preference, and market-linked data, we establish an open benchmark for alternative protein performance to democratize research and accelerate principled data-driven innovation. 

Texture shapes how we perceive and like food, yet clear links between mechanical measurements and sensory perception of texture remain elusive. Here we combine sensory data from a blind tasting involving 101 participants with mechanical texture profile analysis across six burgers to identify the textural features that drive consumer perception and liking. We compare five burgers—generated via artificial intelligence—with animal-based, plant-based, mushroom-based, and hybrid animal-mushroom patties, and the classical Big Mac®. Three main findings emerge: First, animal-based burgers occupy a distinctive and coherent sensory–mechanical region associated with attributes such as firm, fatty, and holds together. Second, mushroom- and plant-based burgers deviate from this region in protein-dependent ways: mushroom-based burgers are associated with springy and gummy textures, while plant-based burgers are associated with dry, brittle, and crumbly textures. Hybrid animal–mushroom burgers, however, maintain sensory profiles comparable to fully animal-based burgers. Third, resilience emerges as the strongest mechanical correlate of perceived meatiness and sensory texture, while stiffness and hardness show no statistically significant association with consumer perception. Texture independently predicts overall liking alongside flavor: increasing texture liking by one point increases overall liking by 0.28. Among all sensory attributes, meatiness is the dominant predictor of texture liking. These findings suggest that resilience may be a promising target for texture engineering and establish texture as a critical design objective for sustainable alternative proteins. 

For fully actuated rigid robots, kinematic inversion is a purely geometric problem, efficiently solved by closed-loop inverse kinematics (CLIK) schemes that compute joint configurations to position the robot body in space. For underactuated soft robots, however, not all configurations are attainable through control action, making kinematic inversion extremely challenging. Extensions of CLIK address this by introducing end-to-end mappings from actuation to task space for the controller to operate on, but typically assume finite dimensions of the underlying virtual configuration space. In this work, we formulate CLIK in the infinite-dimensional domain to reason about the entire soft robot shape while solving tasks. We do this by composing an actuation-to-shape map with a shape-to-task map, deriving the differential end-to-end kinematics via an infinite-dimensional chain rule, and thereby obtaining a Jacobian-based CLIK algorithm. Since this actuation-to-shape mapping is rarely available in closed form, we propose to learn it using differentiable neural operator networks. We first present an analytical study on a constant-curvature segment, and then apply the neural version of the algorithm to a three-fiber soft robotic arm whose underlying model relies on morphoelasticity and active filament theory.