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DOE OSTI · 2586381

First-principles calculations with machine learning modeling to predict high-temperature gas sensing materials

Chong, Leebyn [NETL Site Support Contractor, National Energy Technology Laboratory]·Nguyen, Manh [Oak Ridge Institute for Science and Education (ORISE)]·Lee, Yueh-Lin [NETL Site Support Contractor, National Energy Technology Laboratory]·Wuenschell, Jeffrey [NETL] (ORCID:0000000329898558)·Saidi, Wissam [NETL] (ORCID:0000000167144832)·Sorescu, Dan [NETL] (ORCID:0000000217497629)·Duan, Yuhua [NETL] (ORCID:0000000174470142)
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Abstract

This talk introduced our research on developing sensing materials for harsh environmental applications. By combining first-principles density functional theory simulations with AI/ML approach, we have established a sensor database which can predict candidate materials at given operating conditions.

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density functional theory (DFT)high temperature gas sensorsmachine learningperovskites

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Chong, Leebyn [NETL Site Support Contractor, National Energy Technology Laboratory], Nguyen, Manh [Oak Ridge Institute for Science and Education (ORISE)], Lee, Yueh-Lin [NETL Site Support Contractor, National Energy Technology Laboratory], Wuenschell, Jeffrey [NETL] (ORCID:0000000329898558), Saidi, Wissam [NETL] (ORCID:0000000167144832), Sorescu, Dan [NETL] (ORCID:0000000217497629), Duan, Yuhua [NETL] (ORCID:0000000174470142). 2025-08-18. First-principles calculations with machine learning modeling to predict high-temperature gas sensing materials. https://doi.org/10.2172/2586381

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