The design of next-generation materials requires innovative strategies that combine advanced computational modeling, experimental data, and artificial intelligence to achieve unprecedented functionality, adaptability, and performance. Architected, adaptive, and morphing materials—ranging from metamaterials to bio-inspired structures—offer unique opportunities for tailoring mechanical, thermal, and multifunctional properties through the careful arrangement of geometry, topology, and composition [1-3]. This minisymposium will focus on data-driven approaches to the design and optimization of architected and adaptive materials across scales, encompassing studies at any length scale as well as fully integrated multiscale methodologies. We invite work that explores the integration of AI/ML with physics-based modeling to accelerate material discovery, property prediction, and performance optimization. Topics of interest include, but are not limited to: • Physics-informed and hybrid modeling frameworks for architected materials • Integration of experimental data (e.g., materials imaging) into predictive models • Coupling micro-, meso-, and macro-scale analyses for material and structural design • Data-driven multiscale topology optimization and inverse design • Design under uncertainty, including robust and reliability-based approaches • Adaptive and morphing materials with tunable stiffness, shape, or properties in response to external stimuli • Bio-inspired and functionally graded architectures for enhanced performance A special emphasis will be placed on approaches that explicitly address uncertainty, where the combined use of stochastic modeling, uncertainty quantification, and robust optimization ensures reliable performance in real-world applications. Contributions that demonstrate cross-disciplinary links—such as between aerospace, biomedical devices, soft robotics, and energy systems—are particularly encouraged. By uniting researchers from computational mechanics, materials science, and data science, this minisymposium will provide a platform for sharing cutting-edge research, fostering collaborations, and shaping the future of data-driven design of architected and adaptive materials. The broad yet distinctive focus ensures relevance to participants working at different scales, while highlighting emerging trends in adaptive and intelligent materials. References [1] Kadic et al. 2019 [2] Osanov and Guest 2016 [3] Bishara et al. 2023