Supervised AI and Deep Neural Networks to Evaluate High-Entropy Alloys as Reduction Catalysts in Aqueous Environments

Competitive
surface
adsorption energies on catalytic
surfaces constitute
a fundamental aspect of modeling electrochemical reactions in aqueous
environments. The conventional approach to this task relies on applying
density functional theory, albeit with computationally intensive demands,
particularly when dealing with intricate surfaces. In this study,
we present a methodological exposition of quantifying competitive
relationships within complex systems. Our methodology leverages quantum-mechanical-guided
deep neural networks, deployed in the investigation of quinary high-entropy
alloys composed of Mo–Cr–Mn–Fe–Co–Ni–Cu–Zn.
These alloys are under examination as prospective electrocatalysts,
facilitating the electrochemical synthesis of ammonia in aqueous media.
Even in the most favorable scenario for nitrogen fixation identified
in this study, at the transition from O and OH coverage to surface
hydrogenation, the probability of N2 coverage remains low.
This underscores the fact that catalyst optimization alone is insufficient
for achieving efficient nitrogen reduction. In particular, these insights
illuminate that system consideration with oxygen- and hydrogen-repelling
approaches or high-pressure solutions would be necessary for improved
nitrogen reduction within an aqueous environment.