Coordination Engineering of Dual-Atom Catalysts for Overall Water Splitting: Mechanistic Insights from Constant-Potential First-Principles and Machine Learning
Jiahang Li, Suhang Li, Chong Yan, Jiajun Yu, Qinzhuang Liu, Ruo-Ya Wang, Dongwei Ma

TL;DR
This study combines first-principles calculations and machine learning to design and understand dual-atom catalysts with optimized coordination for efficient overall water splitting.
Contribution
It introduces a systematic approach using coordination engineering and machine learning to identify high-performance bifunctional catalysts for water splitting.
Findings
Identified 24 promising dual-atom catalysts with low overpotentials and PCET barriers.
Demonstrated that coordination engineering enables simultaneous optimization of HER and OER activities.
Revealed structure-performance relationships through machine learning analysis.
Abstract
The rational design of bifunctional electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is essential for achieving efficient and cost-effective overall water splitting. Atomically dispersed transition-metal catalysts, including single-atom catalysts and dual-atom catalysts (DACs), have emerged as a prominent class of heterogeneous catalysts, in which coordination engineering plays a decisive role in tuning catalytic performance. Herein, we explore coordination-engineered bifunctional overall water splitting electrocatalysts using graphene-supported DACs (TM1TM2-C6-xNx) as model systems. By tuning C/N coordination and dual-metal combinations (Fe, Co, Ni, and Cu), a library of 228 structures was constructed. A three-step screening strategy, combining constant-charge and constant-potential density functional theory with kinetic analysis of…
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