  Large volumes of data from material characterizations call for rapid and
automatic data analysis to accelerate materials discovery. Herein, we report a
convolutional neural network (CNN) that was trained based on theoretic data and
very limited experimental data for fast identification of experimental X-ray
diffraction (XRD) spectra of metal-organic frameworks (MOFs). To augment the
data for training the model, noise was extracted from experimental spectra and
shuffled, then merged with the main peaks that were extracted from theoretical
spectra to synthesize new spectra. For the first time, one-to-one material
identification was achieved. The optimized model showed the highest
identification accuracy of 96.7% for the Top 5 ranking among a dataset of 1012
MOFs. Neighborhood components analysis (NCA) on the experimental XRD spectra
shows that the spectra from the same material are clustered in groups in the
NCA map. Analysis on the class activation maps of the last CNN layer further
discloses the mechanism by which the CNN model successfully identifies
individual MOFs from the XRD spectra. This CNN model trained by the
data-augmentation technique would not only open numerous potential applications
for identifying XRD spectra for different materials, but also pave avenues to
autonomously analyze data by other characterization tools such as FTIR, Raman,
and NMR.
