Discovery of complex oxides via automated experiments and data science

Area of Science:

• Materials Science
• Solid State Chemistry
• Computational Materials Science

Background:

• Discovering materials with specific properties is challenging due to the vast number of possible compositions.
• Traditional predictive models struggle to accurately guide the search for novel materials in high-order composition spaces.
• High-throughput experimental methods are crucial for exploring complex material systems.

Purpose of the Study:

• To identify novel regions in three-cation metal oxide composition spaces with emergent optical properties.
• To develop a high-throughput workflow for accelerating the discovery of materials with complex, phase-dependent properties.
• To demonstrate the utility of data-driven approaches in materials science research.

Main Methods:

• Screening of 376,752 distinct compositions across 108 three-cation oxide systems using high-throughput optical property measurements.
• Development of data models for candidate phase diagrams and compositions exhibiting emergent optical properties.
• Close integration of data validation with experimental design to create a robust discovery workflow.

Main Results:

• Identification of novel composition spaces with optical trends not explained by simple phase mixtures.
• Discovery of a Cobalt-Tantalum-Tin (Co-Ta-Sn) substitutional alloy oxide with tunable transparency, catalytic activity, and stability.
• Demonstration of a Co-Ta-Sn oxide's resilience in strong acid electrolytes.

Conclusions:

• High-throughput optical property measurements coupled with data modeling effectively identify promising material compositions.
• The developed workflow accelerates the discovery of materials with complex, phase-dependent properties.
• The discovered Co-Ta-Sn oxide represents a significant advancement in materials with tunable functional characteristics.