Machine learning-optimized bioinspired N-doped carbon-wrapped trimetallic oxides for an efficient oxygen evolution reaction.

Abstract

Despite the significant advancement in the development of a wide range of nitrogen-doped multi-metal oxide-based electrocatalysts for the oxygen evolution reaction (OER), there is a need to investigate the individual contributions of each metal node and the specific role of the N-source in OER performance. However, conventional experimental approaches are often labor-intensive, time-consuming, and inefficient for decoupling the complex synergistic interactions within multi-metallic systems, thereby limiting the development of efficient electrocatalysts. To address this challenge, herein we have employed machine learning (ML) optimization to understand the individual contributions of each metal in the multi-metallic system and N-sources, enabling both the screening and designing of efficient OER electrocatalysts. We have fabricated trimetallic FeCoZn squarate MOFs (FCZ-Sq MOFs), and ML models were systematically employed to optimize and identify the optimal (Fe/Co/Zn) metal node ratio to design highly efficient MOF-based electrocatalysts. The ML-optimized MOF was subsequently decorated with different bio-inspired N-sources (purine (Pu), pyridine (Py), and xanthine (Xn)) and wrapped with polydopamine (PDA), and a second-stage ML optimization was employed to elucidate and select the most effective nitrogen source. The ML-optimized materials were subjected to calcination to form N-doped carbon-coated trimetallic oxides (NC@FCZ-Ox). Among the ML-optimized materials, the Pu-derived catalyst (NCPu@FCZ-Ox) has shown enhanced electrocatalytic activity by exhibiting low overpotential (270 mV at 10 mA cm-2), low onset potential (1.40 V vs. RHE), and Tafel slope (74 mV dec-1) compared to NCPy@FCZ-Ox (1.42 V, 320 mV), NCXn@FCZ-Ox (1.44 V, 340 mV), FCZ-Sq MOF (1.47 V, 355 mV) and NF (1.60 V, 430 mV). Importantly, this work not only demonstrates the effectiveness of ML-assisted optimization in accelerating catalyst discovery but also provides a fundamental understanding of structure-performance relationships in multi-metallic and N-doped systems. To the best of our knowledge, this is the first study to report the impact of ML in precisely optimizing and screening the best metal nodes and N-sources for catalyst design in sustainable energy applications.