Highly Accurate Machine Learning Point Group Classifier for Crystals

Inspired by the remarkable ongoing progress of the data-driven science approach, a predictive model is developed for the crystallographic point group classification of the ternary compounds using machine learning. In this work, the first step is to generate a space of all possible ternary compounds based on the common and uncommon oxidation states of 77 elements. The total number of possible elemental combinations has surpassed 600 million materials. The structures of more than 10 million of these materials were obtained from the NOMAD material database. Finally and starting only from the chemical formula, the elemental properties are utilized to develop an accurate predictive model for the crystallographic point group classification. The average balanced accuracy of the predictive model has exceeded 90%. The success of this work will contribute effectively to the advancement of materials discovery.