Bayesian inference to identify crystalline structures for XRD

TL;DR

This paper introduces a Bayesian inference method for automatically identifying and quantifying crystalline phase structures from X-ray diffraction patterns, reducing analyst dependency and providing confidence intervals.

Contribution

The study develops a Bayesian approach to objectively infer crystalline structures and their proportions from diffraction data, improving accuracy and confidence estimation over conventional methods.

Findings

Successfully identified true crystalline phases with high probability

Estimated mixing ratios accurately for well-crystallized samples

Provided a probabilistic framework for confidence intervals in analysis

Abstract

Crystalline phase structure is essential for understanding the performance and properties of a material. Therefore, this study identified and quantified the crystalline phase structure of a sample based on the diffraction pattern observed when the crystalline sample was irradiated with electromagnetic waves such as X-rays. Conventional analysis necessitates experienced and knowledgeable researchers to shorten the list from many candidate crystalline phase structures. However, the Conventional diffraction pattern analysis is highly analyst-dependent and not objective. Additionally, there is no established method for discussing the confidence intervals of the analysis results. Thus, this study aimed to establish a method for automatically inferring crystalline phase structures from diffraction patterns using Bayesian inference. Our method successfully identified true crystalline phase structures with a high probability from 50 candidate crystalline phase structures. Further, the mixing ratios of selected crystalline phase structures were estimated with a high degree of accuracy. This study provided reasonable results for well-crystallized samples that clearly identified the crystalline phase structures.