Connectionist technique estimates of hydrogen storage capacity on metal hydrides using hybrid GAPSO-LSSVM approach

Abstract

Abstract The AB2 metal hydrides are one of the preferred choices for hydrogen storage. Meanwhile, the estimation of hydrogen storage capacity will accelerate their development procedure. Machine learning algorithms can predict the correlation between the metal hydride chemical composition and its hydrogen storage capacity. With this purpose, a total number of 244 pairs of AB2 alloys including the elements and their respective hydrogen storage capacity were collected from the literature. In the present study, three machine learning algorithms including GA-LSSVM, PSO-LSSVM, and HGAPSO-LSSVM were employed. These models were able to appropriately predict the hydrogen storage capacity in the AB2 metal hydrides. So the HGAPSO-LSSVM model had the highest accuracy. In this model, the statistical factors of R2, STD, MSE, RMSE, and MRE were 0.980, 0.043, 0.0020, 0.045, and 0.972%, respectively. The sensitivity analysis of the input variables also illustrated that the Sn, Co, and Ni elements had the highest effect on the amount of hydrogen storage capacity in AB2 metal hydrides.