利用贝叶斯峰顶拟合从X射线衍射数据快速稳健地构建机器学习就绪的峰特征表
Rapid and Robust construction of an ML-ready peak feature table from X-ray diffraction data using Bayesian peak-top fitting
AI总结：
本研究提出一种基于贝叶斯峰顶拟合的方法，从XRD数据中快速稳健地提取峰特征并构建ML就绪特征表，进而通过贝叶斯回归预测材料性能，发现004峰高和峰面积是关键特征。
摘要：
为了通过数据驱动的科学方法推进材料的发展，必须建立从测量和计算数据中构建机器学习（ML）就绪特征表的适当方法。在材料开发中，X射线衍射（XRD）是分析晶体结构和其它微结构特征的有效技术，这些特征包含可以解释材料性能的信息。因此，在没有分析人员偏差的情况下，从XRD数据中全自动提取峰特征是一个重大挑战。本研究旨在建立一种高效且稳健的方法，从XRD数据中构建符合ML标准（ML就绪）的峰特征表。我们挑战了在仅预先知道峰函数轮廓、而不知道测量材料或晶体结构因子的情况下进行峰特征提取的问题。我们利用贝叶斯估计从XRD数据中提取峰特征，随后进行带特征选择的贝叶斯回归分析以预测材料性能。所提出的方法仅关注局部感兴趣区域（ROIs）内的峰顶，并快速准确地提取峰特征。这一过程促进了从XRD数据中快速提取主要峰特征并构建ML就绪特征表。然后，我们将贝叶斯线性回归应用于最大磁能积$(BH)_{max}$，使用提取的峰特征作为解释变量。结果产生了合理且稳健的回归结果。因此，本研究的发现表明，\textit{004}峰高和峰面积是预测$(BH)_{max}$的重要特征。
英文摘要：
To advance the development of materials through data-driven scientific methods, appropriate methods for building machine learning (ML)-ready feature tables from measured and computed data must be established. In materials development, X-ray diffraction (XRD) is an effective technique for analysing crystal structures and other microstructural features that have information that can explain material properties. Therefore, the fully automated extraction of peak features from XRD data without the bias of an analyst is a significant challenge. This study aimed to establish an efficient and robust approach for constructing peak feature tables that follow ML standards (ML-ready) from XRD data. We challenge peak feature extraction in the situation where only the peak function profile is known a priori, without knowledge of the measurement material or crystal structure factor. We utilized Bayesian estimation to extract peak features from XRD data and subsequently performed Bayesian regression analysis with feature selection to predict the material property. The proposed method focused only on the tops of peaks within localized regions of interest (ROIs) and extracted peak features quickly and accurately. This process facilitated the rapid extracting of major peak features from the XRD data and the construction of an ML-ready feature table. We then applied Bayesian linear regression to the maximum energy product $(BH)_{max}$, using the extracted peak features as the explanatory variable. The outcomes yielded reasonable and robust regression results. Thus, the findings of this study indicated that \textit{004} peak height and area were important features for predicting $(BH)_{max}$.
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