Combining physical models with dynamically acquired experimental information for the optimization of multicomponent NASICON fast ionic conductors in a self-driving laboratory
This paper presents a cost-guided autonomous solid-state synthesis (CASS) framework that integrates physical models with dynamically acquired experimental data to efficiently optimize multicomponent NASICON fast ionic conductors, successfully identifying high-performance compositions with superior ionic conductivity and phase purity in an autonomous laboratory setting.