Future Materials
Future Materials is the lab’s core thrust, focused on designing polymers and composites that perform, adapt, and regenerate, shifting from static strength to dynamic, circular functionality. Integrating polymer chemistry, molecular design, and interface science, the effort advances responsive, energy-active, and resource-resilient materials. Adaptive composites and stimuli-responsive polymers incorporate sensing and self-repair through reversible chemistries, conductive networks, and AI/ML-guided links between chemistry and function. Structural energy materials combine mechanical performance with electrochemical functionality, enabling lightweight, recyclable systems that balance ion transport, stiffness, and thermal management. Sustainable efforts leverage CO₂-derived and renewable NIPU chemistries to create biobased foams and elastomers, validated by TEA and LCA to accelerate industry-ready circular polymers.
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Advanced Manufacturing
Advanced Manufacturing couples processing science, material behavior, and digital infrastructure to enable high-rate, energy-efficient, and sustainable composite manufacturing. Using thermomechanical modeling, real-time analytics, and the Material–Process–Microstructure–Performance (MP²) framework, it replaces trial-and-error with data-driven design. High-rate thermoplastic and thermoset processes are optimized by controlling heat transfer, flow, curing, crystallization, and deformation. Hybrid Single Shot Manufacturing integrates forming, curing, and bonding into one step to create robust in situ interfaces and multimaterial architectures. Self-driving labs combine high-throughput testing, in situ sensing, robotics, and physics-informed machine learning to rapidly map and optimize processing–performance relationships.
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AI/ML in Materials & Manufacturing Processes
AI/ML in Materials and Manufacturing encodes Material–Process–Microstructure–Performance (MP²) relationships to enable data- and physics-driven discovery, design, and deployment of polymers and composites. Physics-informed models integrate multiscale simulation, machine data, and experiments to learn across chemistry, processing, and structure. Real-time sensing and AI optimize design and manufacturing spaces by detecting defects, forecasting process health, and autonomously navigating high-dimensional workflows. Inverse design uses generative models to start from target performance and propose manufacturable materials, microstructures, and processing pathways.
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