Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Perovskites have attracted increasing attention because of their excellent physical and chemical properties in various fields, exhibiting a universal formula of ABO(3) with matching compatible sizes of A-site and B-site cations. In this work, four different prediction models of machine learning algorithms, including support vector regression based on radial basis kernel function (SVM-RBF), ridge regression (RR), random forest (RF), and back propagation neural network (BPNN), are established to predict the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy of perovskite materials. Combined with the fitting diagrams of the predicted values and DFT calculated values, the results show that SVM-RBF has a smaller bias in predicting the crystal volume. RR has a smaller bias in predicting the thermodynamic stability. RF has a smaller bias in predicting the formation energy, crystal volume, and thermodynamic stability. BPNN has a smaller bias in predicting the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy. Obviously, different machine learning algorithms exhibit different sensitivity to data sample distribution, indicating that we should select different algorithms to predict different performance parameters of perovskite materials.