The center's scientific goals will be achieved via iterative efforts of three research thrusts shown above. Research Thrust I is the DLC representing a suite of seamlessly linked, high-fidelity multiscale models for simulating all stages of a polymer composite’s life cycle, which also integrates uncertainty and E3 impact quantification. The DLC will enable the generation of a large quantity of high-fidelity data to train AI models. Research Thrust II is the AI modeling and inverse design research thrust, in which we develop new AI models, including physical informed neural networks (PINN) and multiscale deep neural operators (DeepONet), to efficiently map the composite materials’ architecture and the manufacturing process to composite components’ performance. Next, we develop a conditional VAE neural network (MaterialVAE) for material inverse design and a conditional VAEGAN neural network (ProcessGAN) for manufacturing process design. By utilizing the experimental facility and capabilities at the Clemson Composites Center, the Center for Manufacturing Innovation at the University of Florida, the Pacific Northwest National Laboratory (PNNL), and other relevant BES facilities and infrastructure, in Research Thrust III, we conduct material characterization and testing of mechanical, physical, rheological, and morphological properties at nano-, micro-, and macroscales to inform and validate both DLC and AI models and simulations. We will also implement new/hybrid processes that combine existing or new scalable processing routes to create tailored composite micro and macro structures. Finally, the inverse design is performed by the generative AI models to obtain a holist solution of optimal composite material and its manufacturing process.