Explainable AI for optimizing oxygen reduction on Pt monolayer core–shell catalysts

Unlocking the secrets of catalysis with AI: Leveraging the power of explainable artificial intelligence (XAI), this study rapidly identifies novel Pt monolayer core-shell alloys for enhancing oxygen reduction reaction (ORR) efficiency. By exploring thousands of alloy combinations and employing SHAP analysis, we reveal the critical role of adsorbate resonance energies in chemical bonding, offering new pathways for designing superior catalytic materials.

Abstract

As a subfield of artificial intelligence (AI), machine learning (ML) has emerged as a versatile tool in accelerating catalytic materials discovery because of its ability to find complex patterns in high-dimensional data. While the intricacy of cutting-edge ML models, such as deep learning, makes them powerful, it also renders decision-making processes challenging to explain. Recent advances in explainable AI technologies, which aim to make the inner workings of ML models understandable to humans, have considerably increased our capacity to gain insights from data. In this study, taking the oxygen reduction reaction (ORR) on {111}-oriented Pt monolayer core–shell catalysts as an example, we show how the recently developed theory-infused neural network (TinNet) algorithm enables a rapid search for optimal site motifs with the chemisorption energy of hydroxyl (OH) as a single descriptor, revealing the underlying physical factors that govern the variations in site reactivity. By exploring a broad design space of Pt monolayer core–shell alloys (∼17,000$\sim 17,000$ candidates) that were generated from ∼1500$\sim 1500$ thermodynamically stable bulk structures in existing material databases, we identified novel alloy systems along with previously known catalysts in the goldilocks zone of reactivity properties. SHAP (SHapley Additive exPlanations) analysis reveals the important role of adsorbate resonance energies that originate from sp$sp$-band interactions in chemical bonding at metal surfaces. Extracting physical insights into surface reactivity with explainable AI opens up new design pathways for optimizing catalytic performance beyond active sites.

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