Machine Learning-Driven Crystal System Prediction for Perovskites Using Augmented X-ray Diffraction Data

TL;DR

This paper develops a machine learning framework utilizing various models and data augmentation techniques to accurately predict crystal systems, point groups, and space groups of perovskites from X-ray diffraction data, aiding materials discovery.

Contribution

It introduces a comprehensive ML approach with advanced models and augmentation strategies for crystal structure classification from XRD data, achieving high accuracy and robustness.

Findings

TSF with SMOTE achieved MCC of 0.9 and accuracy of 97.76%

Balanced accuracies above 95% for point and space group prediction

High performance for symmetry-distinct classes like cubic systems and specific point groups

Abstract

Prediction of crystal system from X-ray diffraction (XRD) spectra is a critical task in materials science, particularly for perovskite materials which are known for their diverse applications in photovoltaics, optoelectronics, and catalysis. In this study, we present a machine learning (ML)-driven framework that leverages advanced models, including Time Series Forest (TSF), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a simple feedforward neural network (NN), to classify crystal systems, point groups, and space groups from XRD data of perovskite materials. To address class imbalance and enhance model robustness, we integrated feature augmentation strategies such as Synthetic Minority Over-sampling Technique (SMOTE), class weighting, jittering, and spectrum shifting, along with efficient data preprocessing pipelines. The TSF model with SMOTE augmentation achieved strong performance for crystal system prediction, with a Matthews correlation coefficient (MCC) of 0.9, an F1 score of 0.92, and an accuracy of 97.76%. For point and space group prediction, balanced accuracies above 95% were obtained. The model demonstrated high performance for symmetry-distinct classes, including cubic crystal systems, point groups 3m and m-3m, and space groups Pnma and Pnnn. This work highlights the potential of ML for XRD-based structural characterization and accelerated discovery of perovskite materials