The machine learning based approaches efficiently solve the goal of searching the best materials candidate for the targeted properties. The search for topological materials using traditional first-principles and symmetry-based methods often requires lots of computing power or is limited by the crystalline symmetries. In this study, we present frequency-based statistical descriptors for machine learning-driven topological material's classification that is independent of crystallographic symmetry of wave functions. This approach predicts the topological nature of a material based on its chemical formula. With a balanced dataset of 3910 materials, we have achieved classification accuracies of 82\% with the Support Vector Machine (SVM) model and 83\% with the Random Forest (RF) model, where both models have trained on common frequency based features. We have verified the performances of the models using 5-fold cross-validation approach. Further, we have validated the models on a dataset of unseen binary compounds and have efficiently identified 22 common materials using both the models. Next, we implemented the first-principles approach to confirm the topological nature of these predicted materials and found the topological signatures of Dirac, Weyl, and nodal-line semimetallic phases. Therefore, we have demonstrated that the implications of frequency-based descriptors is a practical and less complex way to find novel topological materials with certain physical post-processing filters. This approach lays the groundwork for scalable, data-driven topological property screening of complex materials.
Ralte et al. (Fri,) studied this question.