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DOE OSTI · 2589517

Lowering the barrier to access information-rich transient kinetic data for machine learning methods

Thompson, Brooklyne Ariana [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Oregon State Univ., Corvallis, OR (United States)]·Wang, Shengguang [Idaho National Laboratory (INL), Idaho Falls, ID (United States)]·Goulas, Konstantinos [Oregon State Univ., Corvallis, OR (United States)]·Kunz, Matthew Ross [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Florida Institute of Technology, Melbourne, FL (United States)] (ORCID:0000000188788274)·Fushimi, Rebecca R. [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000275700234)
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Abstract

Transient kinetic data contain a wealth of information about intrinsic features of a catalyst as well as the reaction mechanism. Currently, high volume transient data is underutilized, and data science methods could both increase the value of information that can be extracted from this data, integrate experimental with theoretical data sources, and accelerate the pace of catalyst technology advancement. Transient kinetic characterizations with simple probe molecules exhibiting reversible adsorption, irreversible adsorption and bulk-surface diffusion are presented as training components for similar experiments with more complex surface reactions. In conclusion, by increasing the availability and accessibility of transient kinetic data through details of its structure and acquisition, we aim to decrease the barrier for data scientists to apply machine learning methods to this valuable data source.

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CatalysisData curationKinetic modelingMachine learningRedox equilibration procedureSimulated flux procedureTemporal analysis of products statistical analysis package (tapsap)Transient kinetic data

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Thompson, Brooklyne Ariana [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Oregon State Univ., Corvallis, OR (United States)], Wang, Shengguang [Idaho National Laboratory (INL), Idaho Falls, ID (United States)], Goulas, Konstantinos [Oregon State Univ., Corvallis, OR (United States)], Kunz, Matthew Ross [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Florida Institute of Technology, Melbourne, FL (United States)] (ORCID:0000000188788274), Fushimi, Rebecca R. [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000275700234). 2025-06-28. Lowering the barrier to access information-rich transient kinetic data for machine learning methods. https://doi.org/10.1016/j.jcat.2025.116306

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