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<p>Single atom catalysts (SACs) are frontier composites maximizing the active phase activity, but require stabilization. This study conducted a high-throughput analysis of 54 pristine MXenes as supports for the 30 3<i>d</i>, 4<i>d</i>, and 5<i>d</i> transition metals (TMs), exploring 1620 cases. First-principles calculations on MXene models showed patterns in the adsorption energies, <i>E</i><sub>ads</sub>, of the TM single-atom (SA), revealing high <i>E</i><sub>ads</sub>, except for <i>d</i><sup>5</sup> or <i>d</i><sup>10</sup> TM electronic configurations. The SA diffusion barriers, <i>E</i><sub>b</sub>, revealed easy diffusions, although in some cases high <i>E</i><sub>b</sub> inhibited aggregation or dispersion. Random forest regressor (RFR) machine learning predicted <i>E</i><sub>ads</sub> with a mean absolute error (MAE) of 0.25 eV, and a regression coefficient of 0.99, showing that the TM cohesive energy is key in <i>E</i><sub>ads</sub> prediction. Here, the RFR model reported a MAE of 0.1 eV, with few MXene and SA properties being important. Our findings provide insights to use MXenes as support for SACs or TM clusters.</p>

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Accurate Prediction of Adsorption and Diffusion Energies of Single Metal Atoms Supported on MXenes from Machine Learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月11日

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  • Daniel Dolz,
  • Sara Pibernat,
  • Ángel Morales-García,
  • Francesc Viñes,
  • Francesc Illas

摘要

Single atom catalysts (SACs) are frontier composites maximizing the active phase activity, but require stabilization. This study conducted a high-throughput analysis of 54 pristine MXenes as supports for the 30 3d, 4d, and 5d transition metals (TMs), exploring 1620 cases. First-principles calculations on MXene models showed patterns in the adsorption energies, Eads, of the TM single-atom (SA), revealing high Eads, except for d5 or d10 TM electronic configurations. The SA diffusion barriers, Eb, revealed easy diffusions, although in some cases high Eb inhibited aggregation or dispersion. Random forest regressor (RFR) machine learning predicted Eads with a mean absolute error (MAE) of 0.25 eV, and a regression coefficient of 0.99, showing that the TM cohesive energy is key in Eads prediction. Here, the RFR model reported a MAE of 0.1 eV, with few MXene and SA properties being important. Our findings provide insights to use MXenes as support for SACs or TM clusters.