Comparative study of machine learning regression algorithms for predicting thermal energy storage performance of metal hydrides with high hydrogen density
Metal hydride thermal/hydrogen energy storage material is considered ideal candidate due to high energy density,wide working temperature range and lack of corrosive pollutants.Multi-component metal hydride alloys can be formed by doping with different elements to obtain various target properties.However,conventional material development method relies on experimental synthesis,having the disadvantages of time-consuming and costly.Data-driven machine learning prediction model is capable of addressing this problem.By comparing varieties of regression algorithms such as least squares regression,least absolute shrinkage and selection operator regression,ridge regression,elastic net regression,supporting vector regression,and random forest regression,the relationship between the microscopic properties of metal hydride materials and their macroscopic formation energy are established.Results show that random forest regression have the best prediction performance,with lowest relative errors on both the training and test sets of 3.078 and 8.201 1,high R-squared values,and great generalization and regression abilities.SHAP analysis reveals extreme and mean value of ground state atom of metal hydride exhibit the greatest SHAP value of 5.56 and 1.26,suggesting their significant influence on the formation energy.Analysis for the prediction value of Mg-base,Ca-base,AB type,AB2 type,and AB5 type metal hydrides shows the highest relative error below 9％,further proving the accuracy and universality of the model for all types of metal hydride.This model can be used to predict the formation enthalpy of unknown datasets.
thermal application of solar energymetal hydridehydrogen and heat storagemachine learningperformance comparison
万方数据
维普
