Combining physical models with dynamically acquired experimental information

Elemental substitution within existing structural frameworks is a widely applied strategy for developing advanced materials. Yet, optimizing target properties while maintaining phase purity usually demands extensive trial-and-error, which becomes substantially inefficient when navigating a complex design space. Here, we introduce a strategy that simultaneously and dynamically assesses composition-dependent synthetic accessibility and target properties via aggregated cost functions that guide autonomous experimentation in a truly self-driving and self-learning mode. Specifically, we developed a cost-guided autonomous solid-state synthesis (CASS) framework and demonstrate its application in the discovery of Na superionic conductor (NASICON) solid electrolytes. CASS successfully optimizes ionic conductivity and phase purity, leading to the identification of 18 promising compositions in 78 trials conducted in an autonomous laboratory, the A-Lab. Among these, we identified two fast-conducting NASICONs yielding total (bulk) ionic conductivity of 0.7 (3.96) and 0.3 (3.17) mS/cm. The successful deployment of CASS reinforces the potential of coupling self-driving autonomous laboratories with physics-informed generative models to accelerate materials discovery.