ML & Physics-Based Property Prediction and Iterative Self-Driving Lab

Leverage machine learning models combined with physics-based property prediction to iteratively explore the materials space using automated, self-driving laboratory platforms, to find things like higher temperature superconductors or topological materials.   New designs are needed to minimize large capital expenditures and integrate flexible, modular components that can be rapidly repurposed for new experiments and are robust to variations and error handling.