Stable dioxin-linked metallophthalocyanine covalent organic frameworks as trifunctional electrocatalysts for overall water splitting and oxygen reduction: A combining density functional theory and machine learning study

Song Lu, Yi Ding, Jiadi Ying, Tiancun Liu, Qing Li, Yong Wu, Yafei Zhao, Zhixin Yu,
Stable dioxin-linked metallophthalocyanine covalent organic frameworks as trifunctional electrocatalysts for overall water splitting and oxygen reduction: A combining density functional theory and machine learning study,
International Journal of Hydrogen Energy,
Volume 187,
2025,
152132,
ISSN 0360-3199,
https://doi.org/10.1016/j.ijhydene.2025.152132.
(https://www.sciencedirect.com/science/article/pii/S0360319925051353)
Abstract: Finding efficient electrocatalysts used in the hydrogen evolution reaction (HER), oxygen evolution and reduction reactions (OER/ORR) is significant. Herein, density functional theory (DFT) calculations were employed to prove the viability of a series of transition metal (TM) atoms anchored dioxin-linked metallophthalocyanine (Pc-TFPN) for HER, OER and ORR. V and CoPc-TFPN monolayers display moderate binding strength for *H adsorption, with ΔG*H of 0.01 and −0.01 eV, respectively. CoPc-TFPN follows Volmer-Heyrovsky mechanism for hydrogen production with energy barrier of 0.40 eV. Co, Rh, and IrPc-TFPN were identified as promising bifunctional OER/ORR catalyst with overpotentials of 0.18/0.36 V, 0.44/0.24 V, and 0.33/0.36 V. Machine learning (ML) study was employed to uncover the underlying correlation between activity and atomic features. Besides the d band center, the number of electrons in the d-orbital plays a crucial role in determining the activity. This work provides guidance for designing single-atom catalysts based on covalent organic frameworks.
Keywords: Single-atom catalysts; Trifunctional catalyst; HER/OER/ORR activity; DFT calculations; Machine learning