Sulei Hu

Achieving long-term catalyst stability remains a grand challenge in catalysis. A recent study combines neural-network potential-based molecular dynamics simulations with decision tree-based interpretable machine learning, unveiling crucial support properties that guide the rational design of sinter-resistant platinum catalysts.

Abstract The metal–oxygen redox behavior governs the performance of transition metal oxides in many electrochemical reactions, especially for RuO2 with the activity–stability paradox in the anode oxygen evolution reaction of proton exchange membrane water electrolyzers. Herein, we modulate the electronic structure of RuO2 near the Fermi level to promote reversible Ru redox while suppressing the oxidative release of lattice oxygen.

Sulei Hu https://orcid.org/0000-0002-5350-9447 and Wei-Xue Li https://orcid.org/0000-0002-5043-3088 Abstract Supplementary Material References and Notes Abstract The stability of supported nanocatalysts is crucial to meet environmental and energy challenges and necessitates fundamental theory to relieve trial-and-error experimentation and accelerate lab-to-fab translation.