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<p>MXenes are promising hydrogen-storage materials with high surface areas and tunable electronic structures, but whether single-atom doping can balance first-layer chemisorption and second-layer physisorption remains unclear. In this work, a combined density functional theory and few-shot machine learning framework was constructed to investigate the thermodynamic stability and two-layer hydrogen adsorption behavior of M<sub>1.89</sub>M′<sub>0.11</sub>X systems. Prediction reliability under limited data was improved by 1000 repeated training runs and an average overfitting-rate evaluation. TabPFN gave the most robust formation-energy prediction, with the lowest overfitting rate of 47.2% and only a 0.042 eV validation–test root mean square error (RMSE) difference; the Voting Regressor predicted first-layer adsorption energy with an RMSE of 0.087 eV, and the XGB classifier identified second-layer Kubas-type adsorption with a test-set area under the receiver operating characteristic curve of 0.952. Formation-energy analysis shows that 5<i>d</i> dopants stabilize MXenes by enhancing metal–nonmetal orbital hybridization. First-layer adsorption is more tunable in carbides and governed by electron number and electronegativity, whereas nitrides more readily form moderate second-layer Kubas interactions. Screening by formation energy, first-layer weakening, and second-layer enhancement identifies Ti<sub>1.89</sub>Zr<sub>0.11</sub>N as the optimal candidate, with a 16.90% increase in gravimetric hydrogen storage capacity, 8.61% first-layer weakening, and <i>E</i><sub>ad2</sub> = −0.234 eV. The regulation patterns were further extended to MBenes.</p>

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Regulation of two-layer hydrogen adsorption in Metal-doped MXenes: few-shot learning, mechanisms, and transferability

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Weizhi Tian,
  • Tiren Peng,
  • Wenhao Yan,
  • Xiangxi Fan,
  • Jiawei Li,
  • Yihang Hu,
  • Tao Zhang,
  • Yuanyuan Chen,
  • Hong Cui,
  • Rong Feng,
  • Hongkuan Yuan

摘要

MXenes are promising hydrogen-storage materials with high surface areas and tunable electronic structures, but whether single-atom doping can balance first-layer chemisorption and second-layer physisorption remains unclear. In this work, a combined density functional theory and few-shot machine learning framework was constructed to investigate the thermodynamic stability and two-layer hydrogen adsorption behavior of M1.89M′0.11X systems. Prediction reliability under limited data was improved by 1000 repeated training runs and an average overfitting-rate evaluation. TabPFN gave the most robust formation-energy prediction, with the lowest overfitting rate of 47.2% and only a 0.042 eV validation–test root mean square error (RMSE) difference; the Voting Regressor predicted first-layer adsorption energy with an RMSE of 0.087 eV, and the XGB classifier identified second-layer Kubas-type adsorption with a test-set area under the receiver operating characteristic curve of 0.952. Formation-energy analysis shows that 5d dopants stabilize MXenes by enhancing metal–nonmetal orbital hybridization. First-layer adsorption is more tunable in carbides and governed by electron number and electronegativity, whereas nitrides more readily form moderate second-layer Kubas interactions. Screening by formation energy, first-layer weakening, and second-layer enhancement identifies Ti1.89Zr0.11N as the optimal candidate, with a 16.90% increase in gravimetric hydrogen storage capacity, 8.61% first-layer weakening, and Ead2 = −0.234 eV. The regulation patterns were further extended to MBenes.