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Precise Electrochemical Sizing of Individual Electro-Inactive Particles
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Toward data-driven predictive modeling of electrocatalyst stability and surface reconstruction.
1Department of Materials Design and Innovation, University at Buffalo, Buffalo, New York 14260, USA.
The Journal of Chemical Physics
|July 29, 2025
Summary
Computational modeling, including machine learning, is crucial for understanding electrocatalyst stability and dynamics. These methods help overcome limitations in experimental techniques, enabling better electrocatalyst design by predicting degradation and reconstruction.
Area of Science:
- Computational catalysis and theoretical surface science.
- Materials science and electrochemistry.
Background:
- Electrocatalyst dissolution and surface restructuring are common, causing activity-stability trade-offs and hindering optimization.
- Current characterization techniques struggle to resolve decomposition kinetics and reconstruction dynamics at electrocatalytic interfaces.
- Atomistic modeling, enhanced by physics-driven machine learning, offers a path to understanding and engineering electrocatalyst behavior.
Purpose of the Study:
- To systematically assess classical and data-driven computational approaches for modeling electrocatalyst stability and dynamics.
- To highlight the achievements and limitations of these methods in terms of throughput, efficiency, accuracy, and scalability.
- To provide a new paradigm for optimizing electrocatalyst design by addressing challenges in surface modeling.
Main Methods:
- Review of theoretical surface science and computational catalysis methods.
- Examination of first-principle simulations, surface sampling, neural network interatomic potentials, and generative deep learning models.
- Analysis of data-driven computational techniques for elucidating interfacial atomistic processes.
Main Results:
- Classical and data-driven approaches have advanced the atom-by-atom understanding of electrocatalyst stability.
- Limitations in throughput, efficiency, accuracy, bias, transferability, and scalability exist across different modeling methods.
- Data-driven techniques show promise in addressing technical challenges in surface modeling for electrocatalyst degradation.
Conclusions:
- Computational modeling is essential for realistic and predictive analysis of electrocatalyst degradation and reconstruction.
- Data-driven methods offer a new paradigm for optimizing dissolution kinetics and restructuring dynamics.
- These approaches are key to the rational engineering of stable and efficient electrocatalysts.
