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

ABSTRACT: 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 catalyst behavior under reaction conditions. Given the type and quality of data available in the scientific literature, there are still open questions on how machine learning can be used by experimentalists working in the field of catalysis to accelerate catalyst design. Here, we present a framework that leverages large language models to extract textual data from known and trusted sources to automatically generate large, but relatively low-fidelity, experimental catalysis data sets across many

SUBMITTER: Farris BR 

PROVIDER: S-EPMC12648316 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature