Ternary crystal structure prediction from composition formula with machine learning method

Abstract

One of the big challenges in materials science is to predict crystal structures for given composition formulae and also to determine a chemical/physical property that has a great influence on the crystal structures in the prediction procedure. Here we propose a novel ternary crystal structure prediction methodology using machine learning method based on the features calculated from nine chemical/physical properties. Our method successfully predicted the crystal system from the composition formulae with the accuracy of 0.682. We also investigated which chemical/physical property is influential in predicting crystal system. The contribution rate of features calculated by Random Forest and the reconstructed models without the features related to a property revealed that stoichiometry, especially the rate of composition, is essential in crystal system prediction. Since this method does not use any first principles calculation, it is expected to shorten the time of prediction, leading to the short screening material candidates and accelerate material design.