Machine Learning-Guided Design of Perovskite Oxides for High-Temperature Oxygen Sensing
- National Energy Technology Laboratory (NETL)
- NETL Site Support Contractor, National Energy Technology Laboratory
- NETL
Presentation slides for 2025 MRS Fall Meeting & Exhibit. Reliable oxygen sensors are vital for high temperature applications including combustion engines, steel production, and petrochemical refining, yet identifying stable, high-performance materials for such environments remain challenge. We apply machine learning (ML) to predict the atmospheric oxygen partial pressure–dependent conductivity of perovskite oxides, combining data from the Materials Project and published datasets.
- Research Organization:
- National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
- Sponsoring Organization:
- USDOE Office of Fossil Energy and Carbon Management (FECM)
- DOE Contract Number:
- ;
- OSTI ID:
- 3023603
- Resource Type:
- Conference presentation
- Conference Information:
- Conference Name: 2025 Materials Research Society Fall Meeting and Exhibit Location: Boston, MA, United States Start Date: 11/30/2025 12:00:00 AM End Date: 12/5/2025 12:00:00 AM
- Country of Publication:
- United States
- Language:
- English
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