Publication date: 28th August 2024
Understanding molecular adsorption behavior on electrode surfaces is crucial for optimizing electrocatalytic processes. Since electrode reactivity is significantly influenced by varying experimental conditions, there is a pressing need for adaptable theoretical frameworks that provide in-depth insights into these phenomena. Modeling surface coverage offers a good balance between accuracy and computational cost in capturing the complexity of the solid-liquid interface [1,2].
In this oral presentation, I will introduce an automated workflow developed in Python for the high-throughput analysis of key molecular adsorbates—specifically hydrogen, oxygen, and hydroxide—that may be present during electrochemical oxidation and reduction reactions in aqueous electrolytes. This approach enables predictive assessments of the resting state of metallic electrode surfaces at different applied potentials and pH values by leveraging the Computational Hydrogen Electrode (CHE) method, which is essential prior to any reactivity study.
This framework is driven by a machine learning calculator trained on DFT data, generated using the Cluster Expansion method [3]. It can predict the surface coverages of pristine Cu, Ag, Au, Ni, and Pt metal surfaces [4]. Future work will explore mixed coverages and extend the model to different surface facets and metal alloys. The model's extrapolating capabilities, powered by a representative featurization of the systems, should allow for the screening of the electrocatalyst resting state across the entire composition range of alloying elements. If the predictions prove accurate, this automated scheme could serve as a rapid Pourbaix diagram generator for any metallic electrode surface.
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