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

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, a machine‐learning‐based approach is developed, which allows it to be determined automatically if an unknown material is of perovskite type from powder X‐ray diffraction. After training a deep‐learning model on a dataset of known compounds, the structure types of new unknown compounds can be predicted using their experimental powder X‐ray diffraction patterns. This strategy is used to distinguish perovskite‐type materials in a series of new hybrid lead halides. After validation, this approach is 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 perovskites versus nonperovskites, crystallographers can learn how to quickly identify low‐dimensional perovskites from X‐ray diffraction patterns.