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We are interested in designing new molecules, materials, and biomaterials for technologically important applications such as energy storage, gas separation, drug delivery and biomedicine, and tribology. We combine statistical mechanical theory and multi-scale computational methods with machine learning and artificial intelligence to improve our fundamental understanding of structure-property relationships and to discover new materials. Specifically, we develop accurate, transferable all-atom and coarse-grained models of polymers, carbohydrates, proteins, lipids, and metals, and we build data-driven as well as agentic artificial intelligence (AI) frameworks that automate and accelerate the development of such models. These models allow us to investigate a broad range of systems, from glycopolymers and thermoresponsive polymers to metal–organic frameworks, alloys, and engineered living materials. The meso-scale nature of these models allows the direct comparison with experiments and improves our understanding towards the existing materials. A deeper molecular-level understanding of these materials, combined with data- and AI-driven design, empowers us to create new hybrid materials with predefined structure and function for next-generation technologies.