Next-Gen Crystal Structure Classification: Harnessing Deep Learning and Machine Learning Fusion

Crystal structure classification is the task of assigning a crystal structure to a given set of experimental data. This is a challenging task, as there are over 2 lacs known crystal structures, and the experimental data can be noisy and incomplete. In this paper, we propose a deep machine learning (DML) fusion approach for automatic crystal structure classification. Our approach uses a RESNET with Naive Bayes Classifier (RNB-NET) to learn features from the experimental data. The neural network is then trained on a dataset of known crystal structures. We evaluate our approach on a dataset of more than five thousand crystal structures. Our approach achieves an accuracy of 95%, which is significantly better than the accuracy of previous methods. Our approach is a promising new method for automatic crystal structure classification. It is fast, accurate, and can be used to classify crystal structures from noisy and incomplete data.