AI 中文总结
本研究以Ag-Au-Pd-Pt合金为对象，探究功函数与高覆盖率吸附能作为析氢反应描述符的有效性，发现二者结合可提升活性预测拟合质量，且均为强预测因子。
AI 中文摘要
析氢反应的活性通常通过氢吸附能与Sabatier原理来解释，但在多金属表面上，该描述图景会变得模糊，因为每种组成会呈现出局部吸附环境的分布。本研究探究此前被证明对单金属表面具有预测信息的裸表面功函数，是否仍是成分复杂表面的活性描述符。研究通过扫描电化学池显微镜对三个组合型Ag-Au-Pd-Pt薄膜材料库进行酸性析氢筛选，采用图神经网络为每种被测成分提供吸附能分布与功函数。仅含功函数的模型可解释大部分活性变异（平均R²_log=0.903），经覆盖率校正的吸附模型也可做到（平均R²_log=0.955），二者均优于稀吸附模型（平均R²_log=0.758）。将功函数与经覆盖率校正的吸附能结合，拟合质量最高（平均R²_log=0.969），但仅比单独使用经覆盖率校正的吸附能有小幅提升。对于这四种金属，经覆盖率校正的吸附能与功函数遵循相似趋势，产生相近的活性排序，因此二者结合仅比单独使用其中一项有少量增益，不过二者均为强预测因子。
英文摘要
Hydrogen-evolution activity is commonly rationalized through hydrogen adsorption energies and the Sabatier principle, yet this descriptor picture becomes ambiguous on multimetallic surfaces, where each composition exposes a distribution of local adsorption environments. Here we investigate whether the bare-surface work function, previously shown to add predictive information for monometallic surfaces, remains an activity descriptor for compositionally complex surfaces. We test this on three combinatorial Ag-Au-Pd-Pt thin-film materials libraries screened for acidic hydrogen evolution by scanning electrochemical cell microscopy. Graph neural networks provide adsorption-energy distributions and work functions for each measured composition. A work-function-only model explained most of the activity variation (mean $R^2_\mathrm{log}$ = 0.903), as did a coverage-corrected adsorption model (mean $R^2_\mathrm{log}$ = 0.955), outperforming dilute adsorption (mean $R^2_\mathrm{log}$ = 0.758). Combining work function and coverage-corrected adsorption yielded the highest fit quality (mean $R^2_\mathrm{log}$ = 0.969), but only a small gain over coverage-corrected adsorption alone. For these four metals the coverage-corrected adsorption energy and work function follow a similar trend, producing similar activity rankings, hence including both adds little beyond either one individually, although both are strong predictors.
Comments26 pages, 11 figures
