Navigating Phase Diagram Complexity to Optimize The Robotic Synthesis of Battery Cathodes and Solid-State Electrolytes

There are a plethora of computationally-predicted battery 'wonder' materials, but only a limited number of them can be successfully synthesized. Efficient synthesis recipes are essential to accelerate the realization and manufacturing of theoretically-predicted electrodes. Oftentimes the solid-state synthesis of multicomponent oxide electrodes is impeded by undesired byproduct phases, which can kinetically trap reactions in an incomplete non-equilibrium state. We present a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy competing byproducts, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides with chemistries representative of intercalation battery cathodes and solid-state electrolytes, we perform 224 reactions spanning 27 elements with 28 unique precursors. Our predicted precursors frequently yield target materials with higher phase purity than when starting from traditional precursors. Robotic laboratories offer an exciting new platform for data-driven experimental synthesis science, from which we can develop new fundamental insights to guide both human and robotic chemists.