  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
($\Delta$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.
