论文标题
PXRD相位分数估计的基于优化的监督学习算法
An Optimization-Based Supervised Learning Algorithm for PXRD Phase Fraction Estimation
论文作者
论文摘要
在粉末衍射数据分析中，相位识别是使用其特征性的Bragg峰来确定样品中晶相的过程。对于多相光谱，我们还必须确定样品中每个相的相对重量分数。机器学习算法（例如，人工神经网络）已应用于在粉末衍射分析中执行此类困难的任务，但通常需要大量的培训样品才能接受。我们已经开发了一种方法，即使使用少量的培训样本，也可以表现良好。我们在标记的训练样品上应用定点迭代算法来估计单相光谱。然后，考虑到未知的样品谱，我们再次使用定点迭代算法来确定最能近似未知样品光谱的单相光谱的加权组合。这些权重是样品的所需相位分数。我们将方法与几种传统的机器学习算法进行了比较。
In powder diffraction data analysis, phase identification is the process of determining the crystalline phases in a sample using its characteristic Bragg peaks. For multiphasic spectra, we must also determine the relative weight fraction of each phase in the sample. Machine Learning algorithms (e.g., Artificial Neural Networks) have been applied to perform such difficult tasks in powder diffraction analysis, but typically require a significant number of training samples for acceptable performance. We have developed an approach that performs well even with a small number of training samples. We apply a fixed-point iteration algorithm on the labelled training samples to estimate monophasic spectra. Then, given an unknown sample spectrum, we again use a fixed-point iteration algorithm to determine the weighted combination of monophase spectra that best approximates the unknown sample spectrum. These weights are the desired phase fractions for the sample. We compare our approach with several traditional Machine Learning algorithms.
