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DOE OSTI · 3006188

Leveraging High-throughput Computation and Machine Learning to Discover and Understand Low-Temperature Fast Oxygen Conductors (Final Technical Report)

Jacobs, Ryan [Univ. of Wisconsin, Madison, WI (United States)]·Morgan, Dane [Univ. of Wisconsin, Madison, WI (United States)]
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Abstract

The major goals of this work are twofold: (1) to enable transformative basic understanding of structure-property-performance relationships governing oxygen transport in oxygen-active materials and (2) facilitate the discovery and rational design of new oxygen-active materials which transport oxygen efficiently at low temperature. Transformative understanding and materials design will be accomplished by synergistically combining materials data mining, machine learning, high-throughput computation and targeted experiments.

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36 MATERIALS SCIENCE97 MATHEMATICS AND COMPUTING

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Jacobs, Ryan [Univ. of Wisconsin, Madison, WI (United States)], Morgan, Dane [Univ. of Wisconsin, Madison, WI (United States)]. 2025-12-03. Leveraging High-throughput Computation and Machine Learning to Discover and Understand Low-Temperature Fast Oxygen Conductors (Final Technical Report). https://doi.org/10.2172/3006188

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36 MATERIALS SCIENCE↗