Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods

Curie temperature (Tc), the second order phase transition temperature, is also one of the important physical properties of perovskite materials. It is a meaningful work to quickly and efficiently predict Tc of new perovskite materials before doing a considerable amount of experimental work. In the work, SVM (support vector machine), RVM (relevance vector machine) and RF (random forest) were employed to establish the prediction models of Tc with the physicochemical parameters, respectively. The results reveal that the three models all have high precision and reliability. According to K-fold cross validation, the SVR model had better prediction performance than the RVM and RF models. Meanwhile, the potential perovskite material with higher Tc was found by using the SVR model integrated with the search strategy of genetic algorithm from the virtual samples. The methods outlined here can provide valuable hints into the exploration of materials with desired property and can accelerate the process of materials design.