Careful analysis of XRD patterns with Attention

TL;DR

This paper introduces an Attention-based neural network method to analyze XRD patterns, automatically extracting key peaks and correlating them with battery properties, enabling adaptive and insightful physical analysis.

Contribution

It presents a novel Attention-based deep learning approach for automatic peak extraction and property correlation in XRD spectra, enhancing physical insight and adaptability.

Findings

Automatically identified significant peaks related to battery properties

Visualized correlations between physical properties and spectral features

Adapted to various physical experimental conditions

Abstract

The important peaks related to the physical properties of a lithium ion rechargeable battery were extracted from the measured X ray diffraction spectrum by a convolutional neural network based on the Attention mechanism. Among the deep features, the lattice constant of the cathodic active material was selected as a cell voltage predictor, and the crystallographic behavior of the active anodic and cathodic materials revealed the rate property during the charge discharge states. The machine learning automatically selected the significant peaks from the experimental spectrum. Applying the Attention mechanism with appropriate objective variables in multi task trained models, one can selectively visualize the correlations between interesting physical properties. As the deep features are automatically defined, this approach can adapt to the conditions of various physical experiments.