XERUS：用于快速XRD相位识别和精化自动化的开源工具
XERUS: An open-source tool for quick XRD phase identification and refinement automation
摘要：
XRD衍射图谱分析是材料科学和材料研究的重点之一。随着数据驱动的材料设计方法的进步，可以快速筛选候选材料，以研究所需的物理性能。自动分析和识别给定模式中存在的阶段的有效方法对于这种新范式的成功至关重要。为了帮助这个过程，提供了用于半自动/自动相位识别的开源Python包Xray Estimation and Refinement Using Similarity（XERUS）。XERUS利用开放的晶体结构数据库，而不是依赖于专有数据库，以获得晶体结构的飞行，然后化学空间不可知论。通过包裹GSASII，它可以自动模拟模式和计算用于相位识别的相似性度量。我们的方法是简单和快速的，但也适用于多相识别，通过耦合的相似性计算与快速细化，然后通过一个迭代的峰值去除过程。XERUS在四个不同的实验数据集中显示出了作用，并且它还与最近提出的用于覆盖Li-Mn-O-F化学空间的混合物数据集的深度学习方法进行了基准测试。XERUS将在https://www.github.com/pedrobcst/Xerus/上免费提供
英文摘要：
Analysis of XRD diffraction patterns is one of the keystones of materials science and materials research. With the advancement of data-driven methods for materials design, candidate materials can be quickly screened for the study of a desired physical property. Efficient methods to automatically analyze and identify phases present in a given pattern, are paramount for the success of this new paradigm. To aid this process, the open source python package Xray Estimation and Refinement Using Similarity (XERUS) for semi-automatic/automatic phase identification is presented. XERUS takes advantages of open crystal structure databases, not relying on proprietary databases, to obtain crystal structures on the fly, being then chemical space agnostic. By wrapping around GSASII, it can automatically simulate patterns and calculate similarity measures used for phase identification. Our approach is simple and quick but also applicable to multiphase identification, by coupling the similarity calculations with quick refinements followed by an iterative peak removal process. XERUS is shown in action in four different experimental datasets and also it is benchmarked against a recently proposed deep learning method for a mixture dataset covering the Li-Mn-O-F chemical space. XERUS will be freely available on https://www.github.com/pedrobcst/Xerus/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
