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

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 M 3 (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 ( R 2 = 0.907), identifying the combination of electron affinity and valence electron count as the most critical activity descriptor.