Machine-learning-accelerated screening of hydrogen evolution catalysts in MBenes materials

Abstract

Machine learning (ML) models combined with density functional theory (DFT) calculations are employed to screen and design hydrogen evolution reaction (HER) catalysts from various bare and single-atom doped MBenes materials. The values of Gibbs free energy of hydrogen adsorption (ΔGH*) are accurately predicted via support vector algorithm only by using simply structural and elemental features. With the analysis of combined descriptors and the feature importance, the Bader charge transfer of surface metal is a key factor to influence HER activity of MBenes. Co/Ni2B2, Pt/Ni2B2, Co2B2, Os/Co2B2 and Mn/Co2B2 are screened from 271 MBenes and MXenes as active catalysts, with the near-zero ΔGH* of 0.089, −0.082, −0.13, −0.087 and −0.044 eV, respectively. Finally, stable Co2B2 and Mn/Co2B2 are considered as the excellent HER catalysts due to |ΔGH*| < 0.15 eV over a wide range of hydrogen coverages (θ from 1/9 to 5/9). The present work suggests that ML models are competitive tools in accelerating the screening of efficient HER catalysts.