Fast-Decoding Algorithm for Electrode Processes at Electrified Interfaces by Mean-Field Kinetic Model and Bayesian...

Area of Science:

• Electrocatalysis
• Materials Science
• Computational Chemistry

Background:

• Understanding the microscopic origins of electrocatalyst activity and selectivity has been a persistent challenge.
• Current methods for analyzing complex electrode processes are often slow and lack detailed kinetic insights.

Purpose of the Study:

• To develop a rapid data-driven approach for extracting key kinetic properties of electrocatalysts.
• To investigate the microscopic electrode processes governing the oxygen reduction reaction (ORR) using platinum-based catalysts.
• To establish a foundation for human-machine collaboration in discovering advanced electrochemical materials.

Main Methods:

• Active data mining combined with a mean-field kinetic model.
• Bayesian data assimilation for statistical analysis of experimental and literature data.
• Analysis of current-potential profiles to extract kinetic parameters.

Main Results:

• Successfully extracted kinetic parameters for the four-electron ORR in HClO4 solution using platinum-based single-crystal electrocatalysts.
• Demonstrated the dynamic behavior of kinetic parameters with respect to overpotential via Bayesian inference.
• Validated the algorithm's effectiveness using both in-house experiments and third-party literature data.

Conclusions:

• A fast-decoding algorithm integrating mean-field kinetics and Bayesian data assimilation offers a powerful data-driven method.
• This approach effectively extracts microscopic features from complex electrochemical data.
• The methodology promises to accelerate the search for high-performance electrocatalytic materials through enhanced human-machine collaboration.