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

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 M1 2 X1 - M2 2 X2 bilayer MXenes using only physical intrinsic features (no density functional theory computational variables). The gradient boosting regression and random forest regression algorithms achieved R 2 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 H 2 . Symbolic regression was used to guide the design of hydrogen adsorption descriptors, and two simple descriptors, ( chi / M 1 ) x ( r / M 2 ) 2 and ( r / M 2 ) 3 ( m / X 1 ) , 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.