Computational Design of Dual-Atom Catalysts for Sustainable Energy Conversion

Dual-atom catalysts (M₁M₂-N-C) are emerging as promising candidates for electrochemical reactions (e.g., oxygen reduction reaction) which are critical for clean and sustainable energy devices. However, due to the large chemical design space, myriad possible structural configurations, and dynamic structure evolutions of the metal centers under reaction conditions, the design of dual-atom catalysts has been challenging and cost-prohibitive for both experiments and computations. Here, using high-throughput density functional theory (DFT) calculations combined with machine learning, we rapidly and efficiently evaluate over 20000 dual-atom catalysts for oxygen reduction reaction. We first generate a DFT database of a subset of the dual-atom catalysts, and validate our computational predictions of the structure, stability, and catalytic activity with experimental data where available. With this benchmarked database, machine learning models based on neural networks were trained and applied to identify promising dual-atom catalysts in the search space which possess higher durability and activity than the state-of-the-art Pt and Fe-N-C single-atom catalysts. Furthermore, additional DFT calculations were performed on selected catalysts to reveal the origin of their improved catalytic performance and establish structure-property-performance relationships. The computational framework developed in this work can be generally extend to other important electrochemical reactions including carbon dioxide reduction reaction and hydrogen evolution reaction for sustainable energy conversion.