September 11, 2025

Machine Learning-Enhanced Design of 2D TM

Key Points

Electrocatalytic activity improves with optimized catalysts, demonstrating significant promise for clean energy applications.
Random Forest Regression model identified promising catalysts, with notable ORR and OER activities at 0.276 V and 0.294 V respectively.
Integration of machine learning with density functional theory offers novel pathways to understand catalyst properties effectively.
Key determinants include transition metal characteristics, influencing the design of efficient electrocatalysts for energy conversion.

Abstract

This study integrates machine learning (ML) and density functional theory (DFT) to systematically investigate the oxygen electrocatalytic activity of two-dimensional (2D) TM3(HXBHYB) (HX/YB = HIB (hexaaminobenzene), HHB (hexahydroxybenzene), HTB (hexathiolbenzene), and HSB (hexaselenolbenzene)) metal-organic frameworks (MOFs). By coupling transition metals (TM) with the above ligands, stable 2D TM3(HXBHYB)@MOF systems were constructed. The Random Forest Regression (RFR) model outperformed the others, revealing the intrinsic relationship between the physicochemical properties of 2D TM3(HXBHYB)@MOF and their ORR/OER overpotentials. Model predictions identified promising systems, including Co3(HXBHYB) and Ir3(HXBHYB), with Co3(HHBHSB) and Co(HIB)2 exhibiting exceptional ORR (ηORR = 0.276 V) and OER (ηOER = 0.294 V) activities. SHAP analysis highlighted the valence electron count and atomic radius of the TM as critical descriptors, with the interaction between coordinating atoms and TM valence electrons governing catalytic activity. This work provides universal design principles for evaluating ORR/OER activities, offering a high-precision, low-cost method for catalyst screening.

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Cite This Study

Xie et al. (Tue,) studied this question.

synapsesocial.com/papers/68c2a9c304ab598fffb89db6 https://doi.org/https://doi.org/10.1021/acs.jpclett.5c02042

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