Accelerating Catalysis Understanding via Large Language Model Data Extraction and Shallow Machine Learning Techniques

Catalysis is inherently complex. The lack of precise knowledge available to experimental researchers about the microenvironment, catalytic sites, mechanisms, and changes that occur under reaction conditions has hindered the effectiveness of deep-learning artificial intelligence algorithms to predict...

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Bibliographic Details
Main Author:	Brianna R. Farris (13230811) (author)
Other Authors:	Kevin C. Leonard (379075) (author)
Published:	2025
Subjects:	Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified still open questions question architectures employed producing multicarbon products predict catalyst behavior extract valuable information extract meaningful information established facts within automatically generate large rapidly process literature precise knowledge available generate novel hypotheses accelerate catalyst development accelerate catalyst design extract textual data experimental catalytic systems utilize machine learning shallow learning models experimental catalytic researchers fidelity data sets experimental researchers machine learning learning models data available scientific literature novel insights model development design experiments catalytic sites catalytic properties data encoding work lies trusted sources require high reaction conditions prompt engineering posthoc interpretability inherently complex experimentalists working electrocatalytic reduction critical role certain threshold carbon dioxide also show
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Accelerating Catalysis Understanding via Large Language Model Data Extraction and Shallow Machine Learning Techniques

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