The AI capabilities at the Center for Nanoscale Materials (CNM) are key to tackling the challenges of ​“Predicting Transformations across Scales” and ​“Extracting Rules from Multimodal Data.” To address the first challenge, Argonne researchers are developing software called Bridging Length-scales via Atomistic Simulation Toolkit (BLAST). This high-performance, user-friendly tool allows users to create their own interatomic models by providing a simplified machine learning framework to generate training data sets, optimize potential functions, and cross-validate model predictions. To address the second challenge, Argonne researchers are developing Fully-Automated Nanoscale to Atomistic Structure from Theory and eXperiment (FANTASTX), which combines atomistic-level modeling with machine learning, especially pattern recognition and multi-objective supervised learning. The result will be a powerful machinery that allows real-time interpretation of materials properties and processes from experiments performed at the CNM and elsewhere.