Topological, or Non-topological? A Deep Learning Based Prediction
Ashiqur Rasul Md Shafayat Hossain Ankan Ghosh Dastider Himaddri Roy M. Zahid Hasan Quazi D. M. Khosru
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 and graph neural network which offers an accuracy of 91.4% and an F1
score of 88.5% in classifying topological vs. non-topological materials,
outperforming the other state-of-the-art classifier models. The incorporation
of the graph neural network encodes the underlying relation between the atoms
into the model based on their own 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
suggested neural network is capable of integrating the atom-specific
topological information into the deep learning model, increasing robustness,
and gain in performance. It is believed that the presented work will be an
efficacious tool for predicting the topological class and therefore enable the
high-throughput search for novel materials in this field.