Combining physical models with dynamically acquired experimental information for the optimization of multicomponent NASICON fast ionic conductors in a self-driving laboratoryNew

This research paper presents a strategy for optimizing multicomponent NASICON (Sodium Superionic Conductor) fast ionic conductors using a self-driving laboratory approach. The method employs aggregated cost functions that simultaneously and dynamically assess composition-dependent synthetic accessibility and target properties. This approach addresses the challenge of maintaining phase purity while optimizing target properties in complex design spaces, reducing the extensive trial-and-error typically required in elemental substitution within existing structural frameworks.