Machine learning-assisted design of carbon nanotube-based single-atom catalysts for hydrogen evolution reaction

Sustainable hydrogen energy offers a promising solution to the growing global energy demand associated with fossil fuel consumption. The development of efficient electrocatalysts for the hydrogen evolution reaction (HER) is important, yet the high computational cost of density functional theory (DFT) limits the rapid screening of candidate materials. In this work, a machine learning-assisted framework integrated with DFT calculations is proposed to systematically investigate the HER performance of carbon nanotube (CNT)-supported single-atom catalysts (SACs). A dataset consisting of hydrogen adsorption free energies (ΔG_H*) was constructed from DFT calculations, including 84 M-N₄-CNT(n, n) models involving 28 transition-metal centers anchored on CNTs with three different chirality indices. Based on selected intrinsic transition-metal features and the CNT chirality index, a random forest regression (RFR) model was identified as the optimal model after comparison with multiple machine learning algorithms for predicting ΔG_H*. The RFR model exhibited excellent predictive accuracy, achieving an R² of 0.98 on the test set. Notably, when applied to previously unseen M-N₄-CNT(7, 7) structures, the model maintained high reliability (R² = 0.96), demonstrating strong generalization capability. Machine learning identified Fe-N₄-CNT(7, 7) as a highly promising HER electrocatalyst, with further DFT-based kinetic analysis showing that it follows a Volmer-Tafel reaction pathway. In addition, the SISSO algorithm was employed to derive an interpretable descriptor for ΔG_H* based on elemental properties, achieving high fitting accuracy across different chirality indices. This descriptor provides an efficient tool for rapid catalyst screening while offering mechanistic insights into the key factors governing HER activity in M-N₄-CNT systems.