Machine learning-based deoxidizer screening for intensified hydrogen production from steam splitting

The design of advanced deoxidizer is the key to promote hydrogen production from chemical looping steam splitting, however, the deoxidizer shows complicated possibility of composition, which results in long duration in material exploitation. In this study, Gibbs free energy change (ΔG) is used as the output of the model, and three machine learning models, Decision Tree, Random Forest, and Gradient Boosting Tree algorithms, are established and optimized for functionalized deoxidizer screening. Results indicate that the Gradient Boosting Tree algorithm shows the best performance, and the RMSE and R2 of the test dataset reach 22.24 and 0.93. Through the analysis of feature importance and Partial Dependence Plots, the effects of different deoxidizer properties on the difficulty of steam splitting can be intuitively displayed. Specifically, 907 groups of feasible deoxidizers were selected from 772,083 ones through the combination of machine learning and high-throughput screening. Steam splitting experiments demonstrate the prediction accuracy of 81.3% by Gradient Boosting Tree model. Several less reported composite deoxidizers such as Y2O3+Fe, SrO + Fe, SrO + Co, Li2O + Fe, Li2O + Co were predicted and experimentally verified. This study provides prominent strategy and new implications for deoxidizer screening, which will substantially promote the sustainable development of steam splitting hydrogen generation.