Expanding Synthesizable Battery Materials Through Computation, Theory and Automation-Integrated Synthesis Platforms

The advancement of high-performance, cost-effective energy storage technologies depends on the discovery and synthesis of solid-state materials that serve as electrodes, electrolytes, coatings, and composites in batteries. Computational methods, artificial intelligence, and design strategies have enabled rapid prediction of promising candidates with desired properties. However, the challenge remains in refining these predictions to materials that can be synthesized using scalable approaches, and that are composed of non-critical elements, making them viable for broader applications. The bottleneck often lies in the lack of synthesis design guidelines and the time-intensive nature of experimental synthesis. In this work, we present two automated synthesis platforms—one for solid-state synthesis and the other for solution-based synthesis—that integrate high-throughput computation, thermodynamics-guided synthesis theories, and robotics-enhanced synthesis and characterization. The solid-state platform focuses on synthesizing high-entropy metal oxides with target structures and varied compositions, systematically investigating the effects of precursor selection and synthesis temperature on phase formation. In parallel, our solution-based approach utilizes Pourbaix diagrams to navigate the synthesis landscape of metal oxides, addressing challenges posed by competing solid phases and aqueous species under different conditions. We employ a robotic solution preparation and hydrothermal synthesis system to accelerate the exploration of a wide range of oxide materials. Our integrated approach minimizes experimental trials while maximizing efficiency, not only refining existing synthesis techniques but also advancing the development of diverse metal oxide-based materials with potential battery applications.