Interpretable X-ray diffraction spectra analysis using confidence evaluated deep learning enhanced by template element replacement

Abstract

X-ray Diffraction analysis is crucial for understanding material structures but is hindered by complex patterns and the need for expert interpretation. Deep learning offers automation in phase identification but faces challenges such as data scarcity, overconfidence in predictions and lack of interpretability. This study addresses these by employing Template Element Replacement to generate a perovskite chemical space containing physically unstable virtual structures, enhancing model understanding of XRD-crystal structure relationships and improving classification accuracy by ~5%. A Bayesian-VGGNet model was developed, achieving 84% accuracy on simulated spectra and 75% on external experimental data, while simultaneously estimating prediction uncertainty. Evaluation using Bayesian methods revealed low entropy values, indicating high model confidence. Quantifying the importance of input features to crystal symmetry, aligning significant features of seven crystal systems with physical principles. These approaches enhance the model’s robustness and reliability, making it suitable for practical applications.