论文标题
将机器学习策略与密度功能理论融合，以加快生成氢的MXENES的发现
Fusing machine learning strategy with density functional theory to hasten the discovery of MXenes for hydrogen generation
论文作者
论文摘要
MXENES的拓扑和组合配置空间的复杂性会引起巨大的设计挑战，这些挑战无法通过传统的实验或常规理论方法来解决。为此，我们从监督的机器学习工具箱（ML）算法建立了一种强大，更广泛的多步骤工作流程，以预测4,500 mm $ $ $^{\ prime} $^{\ prime} $ xt $ _2 $ _2 $ _2 $ type mxenes的氢化反应（HER）的活性，其中选择了25 \％\％的效果（1125 \％），以期为1125个系统的效果（1125 \％） （DFT）计算。作为最理想的ML模型，具有递归特征消除和超参数优化的随机森林回归方法准确，快速地预测了氢吸附的Gibbs自由能（$δ$ g $ _ {h} $），具有低预测性平均绝对误差为0.374 ev。基于这些观察结果，H原子直接吸附在mm $ $^{\ prime} $ x t $ _2 $ _2 $ _2 $ -Type mxenes（site-2）的最外层金属原子层上，NB，V，V，MO，MO，CR，CR，CR，CR，CR，CR，CR，CR，CR，基于碳基于O型的稳固性是高度稳定的，可以在高度稳定稳定，并有效地稳定效果。同行。总体而言，开发的基于ML/DFT的多步骤工作流的身体有意义的预测和见解将为加速筛查，合理的设计和潜在催化剂的发现提供新的途径。
The complexity of the topological and combinatorial configuration space of MXenes can give rise to gigantic design challenges that cannot be addressed through traditional experimental or routine theoretical approaches. To this end, we establish a robust and more broadly applicable multistep workflow from the toolbox of supervised machine learning (ML) algorithms for predicting the hydrogen evolution reaction (HER) activity over 4,500 MM$^{\prime}$XT$_2$-type MXenes, where 25\% of the material space (1125 systems) is randomly selected to evaluate the HER performance using density functional theory (DFT) calculations. As the most desirable ML model, the random forest regression method with recursive feature elimination and hyperparameter optimization accurately and rapidly predicts the Gibbs free energy of hydrogen adsorption ($Δ$G$_{H}$) with a low predictive mean absolute error of 0.374 eV. Based on these observations, the H-atom adsorbed directly on top of the outermost metal atomic layer of the MM$^{\prime}$XT$_2$-type MXenes (site-2) with Nb, V, Mo, Cr and Ti metals composed of carbon based O-functionalization are discovered to be highly stable and active catalysts, surpassing that of commercially available platinum based counterparts. Overall, the physically meaningful predictions and insights of the developed ML/DFT-based multistep workflow will open new avenues for accelerated screening, rational design and discovery of potential HER catalysts.
