What question did this study set out to answer?

This study aims to explore the design of bifunctional electrocatalysts for water splitting using machine learning and density functional theory (DFT).

March 5, 2026

Heteromultimetallic Conductive Metal–Organic Framework as Bifunctional Electrocatalyst for Water Splitting: A Combined DFT and Machine Learning Study

Key Points

This study aims to explore the design of bifunctional electrocatalysts for water splitting using machine learning and density functional theory (DFT).
Conducted theoretical investigation using DFT calculations and machine learning techniques.
Screened 75 potential active sites across 35 c-MOF frameworks.
Identified CoCoZn(HITP)2 as the most efficient bifunctional catalyst.
Analyzed electronic structure to understand catalytic efficiency.
CoCoZn(HITP)2 exhibited a total overpotential of 0.39 V, the lowest among tested catalysts.
Bimetallic configurations were found to outperform trimetallic ones for catalytic efficiency.
Fe sites showed strong performance for the hydrogen evolution reaction, while Co sites enhanced oxygen evolution activity.
Machine learning model achieved R2 = 0.907 for predicting bifunctional activity based on electronic properties.

Abstract

Conductive metal–organic frameworks (c-MOFs) represent a promising platform for designing bifunctional electrocatalysts toward the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). However, identifying optimal compositions through conventional “trial-and-error” approaches remains formidable due to their vast compositional complexity. Here, we present a theoretical investigation that integrates high-throughput density functional theory calculations with machine learning to explore entropy-driven design strategies in 2,3,6,7,10,11-hexaimino-triphenylene (HITP)-based c-MOFs. Systems incorporating Fe, Co, Ni, Cu, and Zn metal centers were found to be thermodynamically and electrochemically stable. Screening 75 potential active sites across 35 M3(HITP)2 frameworks identified CoCoZn(HITP)2 as the most efficient bifunctional catalyst with a total overpotential of 0.39 V. Counterintuitively, bimetallic configurations systematically outperformed their higher-entropy trimetallic analogues, revealing that optimal electronic synergy supersedes configurational entropy in governing catalytic efficiency. Electronic structure analysis revealed that the near-ideal orbital energy alignment between Fe d-states and H s-states renders Fe sites intrinsically favorable for HER. Concurrently, the Co d-band center in CoCoZn(HITP)2 suffer an downshift and enhanced electron transfer to *OH intermediates, thus strengthening OER activity. Finally, the stacking ensemble machine learning framework provides a reliable model for bifunctional activity prediction (R2 = 0.907), identifying the combination of electron affinity and valence electron count as the most critical activity descriptor.

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