MEM-C is focused on AI-driven materials discovery with the “MEM-C AI Core”. AI Core faculty provide MEM-C experimental groups with research assistance and are embedded directly in IRGs, ensuring deep integration with the Center’s research activities. As a Shared Facility, the MEM-C AI Core is planning to deploy data-driven exploration strategies, specifically Bayesian-based and Reinforcement Learning (RL) (a class of machine learning (ML) approaches) for data fusion of the multi-modal characterization and adaptive exploration of multi-dimensional process-structure-property spaces. Additionally, the AI Core plans to integrate collaborative ML agents operating and interacting seamlessly between virtual and physical environments. Virtual agents will operate in silico in parallel-computing environments performing ab initio calculations to predict materials properties based on atomic composition and structure. These RL agents will explore an interactive environment – here, a high dimensional feature space that may include a material’s composition, chemical identity, strain, pressure, stacking order, external field, energy levels, etc., as well as processing variables, using a unified data representation of these inputs – by adaptive trial-and-error experimental and/or computational feedback.