Prediction of Pressure-Composition-Temperature Curves of AB2-Type Hydrogen Storage Alloys by Machine Learning

Pressure-composition-temperature (PCT) curves for hydrogen absorption and desorption of AB(2)-type hydrogen storage alloys at arbitrary temperatures are predicted by three machine learning models such as random forest, K-nearest neighbor and deep neural network (DNN). Two data generation methods are adopted to increase the number of data points. A new form of the PCT curve functions is suggested to fit experimental data, which greatly helps improve the prediction accuracy. A van't Hoff type equation is used to generate unmeasured temperature data, which improves the model performance on the PCT behavior at various temperatures. The results indicate that a DNN is the best model for predicting the PCT behavior with a high average correlation value R-2 = 0.93070.

Automated summary

This study predicts the PCT curves for hydrogen absorption and desorption of AB(2)-type hydrogen storage alloys at arbitrary temperatures using three machine learning models. By improving the PCT curve function and generating unmeasured temperature data, the prediction accuracy is greatly improved, with the DNN model performing the best.