Abstract
Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology with graph neural network which offers an accuracy of 91.4% and an F1 score of 88.5% in classifying topological versus non-topological materials, outperforming the other state-of-the-art classifier models. Additionally, out-of-distribution and newly discovered topological materials can be classified using our method with high confidence. The incorporation of the graph neural network encodes the underlying relation between the atoms into the model based on their crystalline structures and thus proved to be an effective method to represent and process non-Euclidean data like molecules with a relatively shallow network. The persistent homology pipeline in the proposed neural network integrates a topological analysis of crystal structures into the deep learning model, enhancing both robustness and performance. Our classifier can serve as an efficacious tool for predicting the topological class, thereby enabling a high-throughput search for fascinating topological materials.