Predicting and understanding photocatalytic CO₂ reduction reaction with IR spectroscopy-based interpretable machine learning framework.

ABSTRACT: The highly selective conversion of carbon dioxide into value-added products is extremely valuable. However, even with the aid of in situ characterization techniques, it remains challenging to directly correlate extensive spectral data carrying microscopic information with macroscopic performance. Herein, we adopted advanced machine learning (ML) approaches to establish an accurate and interpretable relationship between vibrational spectral signals and catalytic performances to uncover hidden physical insights. Focusing on photocatalytic CO₂ reduction, our model is shown to effectively and accurately predict the CO production activity and selectivity based solely on the infrared (IR) spectral signals, the generalizability of which is additionally demonstrated with a new Bi_5<

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11389833 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature