Machine-learning-assisted hydrogen adsorption descriptor design for bilayer MXenes

Abstract

Currently, most of the MXene hydrogen storage materials with excellent performances are screened by empirical trial-and-error methods. All of them are single-layer materials, and they have difficulty meeting actual demands. Herein, we report the accurate prediction of hydrogen adsorption energies for three adsorption modes inside M12X1–M22X2 bilayer MXenes using only physical intrinsic features (no density functional theory computational variables). The gradient boosting regression and random forest regression algorithms achieved R2 of 0.957/0.946 and 0.952/0.935 for chemisorption and physical adsorption models on the training/test set, respectively. In particular, the presence of a nanopump effect mechanism in the MXenes with a small layer spacing ensured that the system had a strong Kubas adsorption of H2. Symbolic regression was used to guide the design of hydrogen adsorption descriptors, and two simple descriptors, (χ/M1)×(r/M2)2 and (r/M2)3(m/X1), were identified to be applied to chemisorption and physical adsorption, respectively. The results could provide a theoretical basis for the subsequent synthesis of MXene materials with excellent hydrogen storage properties.