Perovskite or Not Perovskite? A Deep Learning Approach to Automatically Identify New Hybrid Perovskites from X-ray Diffraction Patterns.

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Abstract

Determining the crystal structure is a critical step in the discovery of new functional materials. This process is time consuming and requires extensive human expertise in crystallography. Here, we developed a machine learning based approach which enables to determine automatically if an unknown material is of perovskite type from powder X-ray diffraction patterns. After training a deep learning model on a dataset of known compounds, the structure types of new unknown compounds could be predicted using their experimental powder X-ray diffraction patterns. This strategy was used to distinguish perovskite-type materials in a series of new hybrid lead halides. After validation, our approach was shown to accurately identify perovskites (accuracy of 92% with convolutional neural network). From the identification of the key features of the patterns used to discriminate perovskite versus non-perovskite, crystallographers could learn how to quickly identify low dimensional perovskites from X-ray diffraction patterns. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.