Machine learning and symbolic regression investigation on stability of MXene materials

Abstract Materials stability is a fundamental parameter that should be considered in almost all materials researches. In this manuscript, we employ machine learning techniques and symbolic regression to investigate material stabilities, focusing on the An+1Bn-type prototypical MXenes. Based on a small dataset, the machine learning algorithms including Random forest, KNN, Logistic regression, SVM and GaussianNB are investigated to evaluate the MXene stabilities, with the SVM algorithm achieving the best accuracy for the classification purpose. More importantly, the symbolic regression is verified to be a viable method to identify proper descriptors and construct new descriptors that correlate with the MXene material stability. This study demonstrates the viability of the machine learning and symbolic regression methods to classify materials and describe materials stability.