Mixture-of-Experts Ensemble with Hierarchical Deep Metric Learning for Spectroscopic Identification

A mixture-of-experts ensemble of hierarchical deep metric learning models is introduced in order to identify materials from X-ray diffraction spectra. In previous studies, the identification accuracy of the 1D convolutional neural networks model deteriorates significantly as the number of classes increases. To overcome this problem, a hierarchical deep metric learning model was developed that can identify approximately 10,000 classes with an average top-1 accuracy of 87%. Furthermore, this new model was employed to create expert models for 73 general chemical elements, which in turn were used to construct a mixture-of-experts ensemble. This ensemble model successfully identified materials from 136,899 classes with a top-1 accuracy of 98%.