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.

翻译：晶体相结构对于理解材料的性能和性质至关重要。因此，本研究基于晶体样品在X射线等电磁波照射下观测到的衍射图谱，对样品的晶体相结构进行了识别与定量分析。传统分析方法需要经验丰富的研究人员从众多候选晶体相结构中缩小范围，但该方法高度依赖分析者且缺乏客观性。此外，目前尚无关于分析结果置信区间讨论的成熟方法。为此，本研究旨在建立一种基于贝叶斯推断从衍射图谱自动推理晶体相结构的方法。我们的方法成功地从50个候选晶体相结构中以高概率识别出真实的晶体相结构，并对所选晶体相结构的混合比例实现了高精度估计。本研究为能够清晰识别晶体相结构的良好结晶样品提供了合理的结果。