Abstract 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. Program summary Program title: PyFitIt. Program Files doi: http://dx.doi.org/10.17632/ydkgfdc38t.1 Licensing provisions: GNU General Public License 3. Programming language: Python, Jupyter Notebook framework. Nature of problem: Quantitative structural refinements of the X-ray absorption near-edge structure spectra (XANES). Identification of the pure spectral and concentration profiles associated with an experimental XANES dataset. Solution method: The fitting procedure of the experimental XANES spectra or of their differences is realized by means of the inverse and direct approaches based on the training set and approximation machine learning algorithms. The spectral resolution method is based on the PCA technique involving the usage of a target transformation matrix. Additional comments including restrictions and unusual features: The current version is compatible with the free FDMNES program package for XANES simulations. However, users can prepare their own matrices of spectra calculated by an arbitrary software and the corresponding structural parameters to perform the fitting procedure in PyFitIt. The complete set of examples is distributed along with the program. References: PyFitIt web page: http://hpc.nano.sfedu.ru/pyfitit/

中文翻译：

---

PyFitit：使用机器学习算法定量分析 XANES 光谱的软件

摘要 由于新型同步辐射光源的开发，X 射线吸收近边光谱 (XANES) 正成为材料科学中极为流行的工具。它提供有关操作和极端条件下感兴趣的原子周围的电荷状态和局部几何形状的信息。然而，与 X 射线衍射相反，研究论文中很少对 XANES 光谱进行定量分析。原因必须是与衍射图相比，计算单个光谱所需的时间更长。对于这种耗时的计算，在多个结构参数的空间中，我们开发了一种最初由 Smolentsev 和 Soldatov (2007) 提出的插值方法。该软件的当前版本，名为 PyFitIt，是 FitIt 的主要升级版本，基于机器学习算法。我们选择 Jupyter Notebook 框架是为了对用户友好，同时可用于重新制作。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，由主成分恢复的纯光谱轮廓由理论插值光谱拟合。我们为逼近和学习算法实现了不同的节点选择方案，包括随机树的梯度提升、径向基函数和神经网络。可以对 XANES 光谱或差分光谱执行拟合程序，从而最大限度地减少理论模拟的系统误差。在直接和间接方法的框架中解决了几个局部最小值的问题。程序概要 程序名称：PyFitIt。程序文件 doi：http://dx.doi.org/10.17632/ydkgfdc38t.1 许可条款：GNU 通用公共许可证 3. 编程语言：Python、Jupyter Notebook 框架。问题性质：X 射线吸收近边结构光谱 (XANES) 的定量结构改进。鉴定与实验 XANES 数据集相关的纯光谱和浓度曲线。求解方法：通过基于训练集和近似机器学习算法的逆和正方法实现实验XANES光谱或其差异的拟合过程。光谱分辨率方法基于涉及使用目标变换矩阵的 PCA 技术。包括限制和不寻常功能在内的其他评论：当前版本与用于 XANES 模拟的免费 FDMNES 程序包兼容。但是，用户可以准备自己的由任意软件计算的光谱矩阵和相应的结构参数，以在 PyFitIt 中执行拟合程序。完整的示例集随程序一起分发。参考资料：PyFitIt 网页：http://hpc.nano.sfedu.ru/pyfitit/ 用户可以准备自己的由任意软件计算的光谱矩阵和相应的结构参数，以在 PyFitIt 中执行拟合程序。完整的示例集随程序一起分发。参考资料：PyFitIt 网页：http://hpc.nano.sfedu.ru/pyfitit/ 用户可以准备自己的由任意软件计算的光谱矩阵和相应的结构参数，以在 PyFitIt 中执行拟合程序。完整的示例集随程序一起分发。参考资料：PyFitIt 网页：http://hpc.nano.sfedu.ru/pyfitit/