Transfer Learning in Inorganic Compounds’ Crystal Structure Classification

Abstract

Deep learning consists of deep convolutional layers and an unsupervised feature selection phase. The feature selection of deep learning on a large size dataset can be employed in correlated prediction models with small size datasets. This methodology is titled deep transfer learning model and enhances prediction model generalization. In this research, we proposed a prediction model for the crystal structure classification of inorganic compounds. Deep learning models in structure classification are usually trained using a large size dataset of 300 K compounds from different quantum compounds dataset (DS1). The feature selection of the deep learning models is reused for selecting features in a small size dataset (with 30 K inorganic compounds and containing 150 different crystal structures) and three alloy classes. The selected features are then fed into a random decision forest prediction model as input. The proposed convolutional neural network (CNN) with transfer learning realizes an accuracy of 98.5%. The experiment results display the CPU time consumed by our model, comparing the time required by similar models. The CPU classification time of the proposed model is 21 s on average.