Accelerating 2D MXene catalyst discovery for the hydrogen evolution reaction by computer-driven workflow and an ensemble learning strategy†
2D MXene materials have the advantages of versatile chemical composition, tunable layer thickness, and facile functionalization nature, and can be used as catalysts for the hydrogen evolution reaction (HER). However, tuning the thermal stability and activation of in-plane activity remain a challenge. We apply high-throughput density functional theory (DFT) calculations, together with a machine learning framework, to identify 2D MXene ordered binary alloy (OBA) activity trends and guide HER catalyst design. 2D MXenes of Mn+1XnO2 (n = 1, 2, 3; X = C, N) and OBA HER catalysts of M2M′X2O2 and M2M′2X3O2 with 3d, 4d and 5d transition metal electrons were enumerated by screening, followed by catalytic activity, thermal stability, and conductivity computations. Our results indicate that 110 kinds of experimentally unexplored 2D MXene OBAs with thermostability and outstanding HER activity surpassing that of noble metal platinum were selected. Especially, the titanium element is mainly contained in the ideal catalysts of 2D MXene OBAs, which is consistent with the MXenes synthesized by experiments. Further, we show that descriptors developed using the AdaBoost ensemble learning model could accurately predict and uncover the essential geometric and chemical origin of HER activity, which is very consistent with the electronic insights. The advanced research strategy, which combines high-throughput computing with machine learning, shows robust ability for evaluating the activity trends and designing new complicated catalysts.