Discovery of complex oxides via automated experiments and data science

The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, where materials discovery efforts have been met with the dual challenges of a combinatorial explosion in the number of candidate materials and a lack of predictive computation to guide experiments. The traditional approach to predictive materials science involves establishing a model that maps composition and structure to properties. We explore an inverse approach wherein a data science workflow uses high throughput measurements of optical properties to identify the composition spaces with interesting materials science. By identifying composition regions whose optical trends cannot be explained by trivial phase behavior, the data science pipeline identifies candidate combinations of elements that form 3-cation metal oxide phases. The identification of such novel phase behavior elevates the measurement of optical properties to the discovery of materials with complex phase-dependent properties. This conceptual workflow is illustrated with Co-Ta-Sn oxides wherein a new rutile alloy is discovered via data science guidance from the high throughput optical characterization. The composition-tuned properties of the rutile oxide alloys include transparency, catalytic activity, and stability in strong acid electrolytes. In addition to the unprecedented mapping of optical properties in 108 unique 3-cation oxide composition spaces, we present a critical discussion of coupling data validation to experiment design to generate a reliable end-to-end high throughput workflow for accelerating scientific discovery.

• Data Mining and Modeling

• General Science