MXenes doped with non-metallic and transition metal elements exhibit remarkable potential as catalysts in the hydrogen energy. Nonetheless, efficiently identifying viable materials from a vast array of candidates remains a formidable challenge. Here, we conducted density functional theory (DFT) calculations to obtain the hydrogen adsorption free energy ($∆G_{\mathrm{H}}$) of 78 types of doped TiVCO₂ MXene catalysts. Then we employed machine learning models to categorize the $∆G_{\mathrm{H}}$ values of the 78 catalysts, resulted in an accurate model which only uses 7 readily available elemental features but has an impressive accuracy of 93.6%. Our model successfully predicting 5 TiVCO₂ catalysts doped with S with superior performance, subsequently validated through DFT calculations. This classification methodology not only evaluates the range of $∆G_{\mathrm{H}}$ effectively but also facilitates qualitative prediction and screening of catalysts, presenting a novel approach for catalytic systems with limited available data.

中文翻译：

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通过 DFT 和机器学习集成增强改性 MXenes 的 HER 催化剂筛选


掺杂非金属和过渡金属元素的 MXenes 在氢能中表现出作为催化剂的巨大潜力。尽管如此，从大量候选材料中有效地识别可行的材料仍然是一项艰巨的挑战。在这里，我们进行了密度泛函理论 （DFT） 计算，获得了 78 种掺杂 TiVCO ₂ MXene 催化剂的氢吸附自由能 （ $∆G_{\mathrm{H}}$ ）。然后，我们采用机器学习模型对 $∆G_{\mathrm{H}}$ 78 种催化剂的值进行分类，从而得到一个准确的模型，该模型仅使用 7 个现成的元素特征，但具有令人印象深刻的 93.6% 的准确率。我们的模型成功预测了 5 种掺杂 S 的 TiVCO ₂ 催化剂，性能优异，随后通过 DFT 计算进行了验证。这种分类方法不仅评估 $∆G_{\mathrm{H}}$ 了有效范围，还促进了催化剂的定性预测和筛选，为可用数据有限的催化系统提供了一种新方法。