Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Abstract

Efficient synthesis recipes are needed both to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Oftentimes the solid-state synthesis of multicomponent oxides 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 materials 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 science, from which we can develop new insights into materials synthesis for both robot and human chemists.

References in corpus (5)

• On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
• Autonomous synthesis of metastable materials
• Observing and modeling the sequential pairwise reactions that drive solid-state ceramic synthesis
• Similarity of Precursors in Solid-state Synthesis as Text-Mined from Scientific Literature
• Chemical Synthesis and Materials Discovery

Cited by in corpus (1)

• Dara: Automated multiple-hypothesis phase identification and refinement from powder X-ray diffraction