PyFitit: The software for quantitative analysis of XANES spectra using machine-learning algorithms

X-ray absorption near-edge spectroscopy (XANES) is becoming an extremely popular tool for material science thanks to the development of new synchrotron radiation light sources. It provides information about charge state and local geometry around atoms of interest in operando and extreme conditions. However, in contrast to X-ray diffraction, a quantitative analysis of XANES spectra is rarely performed in the research papers. The reason must be found in the larger amount of time required for the calculation of a single spectrum compared to a diffractogram. For such time-consuming calculations, in the space of several structural parameters, we developed an interpolation approach proposed originally by Smolentsev and Soldatov (2007). The current version of this software, named PyFitIt, is a major upgrade version of FitIt and it is based on machine learning algorithms. We have chosen Jupyter Notebook framework to be friendly for users and at the same time being available for remastering. The analytical work is divided into two steps. First, the series of experimental spectra are analyzed statistically and decomposed into principal components. Second, pure spectral profiles, recovered by principal components, are fitted by theoretical interpolated spectra. We implemented different schemes of choice of nodes for approximation and learning algorithms including Gradient Boosting of Random Trees, Radial Basis Functions and Neural Networks. The fitting procedure can be performed both for a XANES spectrum or for a difference spectrum, thus minimizing the systematic errors of theoretical simulations. The problem of several local minima is addressed in the framework of direct and indirect approaches.

X射线吸收近边光谱学（XANES）凭借新型同步辐射光源的发展，已成为材料科学领域一项极为流行的工具。该技术能够提供关于感兴趣原子在操作和极端条件下的电荷状态及局部几何结构的信息。然而，与X射线衍射相比，XANES光谱的定量分析在研究论文中鲜有实施。原因在于，相较于衍射图，计算单个光谱所需的时间更长。针对这一耗时计算，在多个结构参数的空间中，我们开发了一种插值方法，该方法最初由Smolentsev和Soldatov于2007年提出。当前版本的软件，名为PyFitIt，是FitIt的重大升级版本，并基于机器学习算法。我们选择了Jupyter Notebook框架，以使用户体验更加友好，同时便于重制。分析工作分为两个步骤。首先，对一系列实验光谱进行统计分析，并将其分解为主成分。其次，通过主成分恢复的纯光谱轮廓，使用理论插值光谱进行拟合。我们实现了不同的节点选择近似方案和学习算法，包括随机森林的梯度提升、径向基函数和神经网络。拟合过程既可针对XANES光谱，也可针对差分光谱进行，从而最小化理论模拟的系统误差。在直接和间接方法的框架下，解决了多个局部最小值的问题。