Prediction of Oxygen Evolution Activity for NiCoFe Oxide Catalysts via Machine Learning

Recent Advances in Iridium-based Electrocatalysts for Acidic Electrolyte Oxidation

Ongoing research to develop advanced electrocatalysts for the oxygen evolution reaction (OER) is needed to address demand for efficient energy conversion and carbon-free energy sources. In the OER process, acidic electrolytes have higher proton concentration and faster response than alkaline ones, but their harsh strongly acidic environment requires catalysts with greater corrosion and oxidation resistance. At present, iridium oxide (IrO2) with its strong stability and excellent catalytic performance is the catalyst of choice for the anode side of commercial PEM electrolysis cells. However, the scarcity and high cost of iridium (Ir) and the unsatisfactory activity of IrO2 hinder industrial scale application and the sustainable development of acidic OER catalytic technology. This highlights the importance of further research on acidic Ir-based OER catalysts. In this review, recent advances in Ir-based acidic OER electrocatalysts are summarized, including fundamental understanding of the acidic OER mechanism, recent insights into the stability of acidic OER catalysts, highly efficient Ir-based electrocatalysts, and common strategies for optimizing Ir-based catalysts. The future challenges and prospects of developing highly effective Ir-based catalysts are also discussed.

Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt₃Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt₃Ir, and Pt₃Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d-band center metric of binding sites.

Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide Electrocatalysts

Electrocatalytic water splitting is an attractive way to generate hydrogen and oxygen for obtaining clean energy. Oxygen evolution reaction (OER), as one of the half reactions of oxygen evolution, is kinetically unfavorable involving the transfer of four electrons. Hydroxides are promising candidates for efficient OER electrocatalysts toward water splitting because of their high intrinsic activity and active surface area. However, quantitative prediction of hydroxide electrocatalytic performances from high-dimensional component spaces remains a challenge, severely hindering the performance-oriented precise composition and process design. Herein, we introduce a machine learning-based OER activity prediction method for hydroxide catalysts under extensive doping space for the first time. The relationship among composition, morphology, phase, pH value of the electrolyte, type of the working electrode, and overpotential was successfully fitted by the random forest algorithm. The model shows a good precision on the forecast of new experiments with a mean relative error of 4.74%. Furthermore, a new high-activity hydroxide catalyst Ni_0.77Fe_0.13La_0.1 was rationally designed and experimentally prepared, showing an ultra-low OP of 226 mV for a current density of 10 mA cm^-2. This work provides an effective and novel way for hydroxide electrocatalyst prediction, which can further enhance the electrocatalyst design toward high catalytic performance.