XERUS: An Open‐Source Tool for Quick XRD Phase Identification and Refinement Automation

Abstract

Analysisof X-ray 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 advantage 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 scriptable, it can automatically simulate patterns and calculate similarity measures used for phase identification. This 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.

Harnessing GPT-3.5 for text parsing in solid-state synthesis – case study of ternary chalcogenides

“…For construction of the Silver Standard dataset using GPT-3.5 to extract information instead, heuristic estimations based on text-available information is more challenging since GPT has not been trained as a chemist. Analyzing diffraction information automatically requires further expertise in automated plot digitization 41 and refinement, 42 which is outside the scope of this work. Following the development of GPT-4 and further, one could anticipate its development to deal with more complicated data including images, illustration, plots, and learning domain expertise in the near future.…”

optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs

“…The Xerus (X-ray Estimation and Refinement Using Similarity) 9,64,93,132 software package implements procedures to refine and screen measured X-ray diffraction patterns of inorganic crystals against databases of crystal structures reported in the literature and beyond. By querying for all possible structures in a given chemical space, Xerus excels at multiphase fits and performs competitively against more specialised and compute-intensive models constructed with machine learning.…”

Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning

“…Deep-learning algorithms have recently gained increasing popularity for assisting XRD interpretation, as they can effectively extract the latent information that is often hard to manually capture from the diffraction patterns. − Early pioneering studies have primarily focused on the autonomous inference of structural attributes from the XRD patterns, including lattice parameters, space group, − and crystallographic dimensionality . These attributes can be fed to the traditional rule-based approaches to speed up the process of XRD analysis.…”