# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 1 Introduction
### Motivation for Machine Learning in XRD Data Analysis  
The motivation for incorporating machine learning (ML) techniques into the analysis of XRD patterns stems from the increase in volume of available data and the need for accurate phase identification, as well as the quantification of multiphase mixtures with varying raw data quality. The following points highlight the advantages of ML over traditional methods of XRD data analysis:  
* Handling big data: The development of synchrotrons has enabled the fast acquisition of XRD patterns, which results in a significant increase in the amount of data collected during experiments. The fine-tuning of beam-time experiments depends on the analysis of patterns, and, thus, an automatic processing flow would be required to further increase its autonomy. In this regard, machine learning routines using clustering represent a potential solution to the challenges faced by the scientific community [70];
* Automated phase identification: In traditional XRD data analysis, the manual identification of phases in complex samples can be time consuming and error prone, especially when dealing with overlapping peaks or noisy data recorded in cases in which short measurement times are a must. ML algorithms can accurately identify and quantify phases and even predict material features from XRD patterns. Moreover, the successful implementation of the algorithms would save time while also benefitting XRD users who are not experts [71; 72; 73];
* Quantitative phase analysis (QPA): Several traditional methods with different complexity and sample preparation requirements are available for the evaluation of phase fractions, including the reference intensity ratio (RIR) method, which requires the introduction of an internal standard calibration [74]; the whole pattern fitting procedure [51; 52; 53; 54]; or Rietveld refinement [54]. Each of the traditional methods is time consuming and requires trained personnel to deliver accurate results. ML algorithms, such as regression models and support vector machines, can efficiently estimate phase proportions based on trained patterns, greatly improving the accuracy and speed of QPA [75; 76].  
The main goal of this study was to assess currently available ML methods for XRD data analysis, their applications, challenges, and limitations, as well as future directions and emerging trends. For this purpose, all results obtained after a search procedure in the Web of Science and Scopus databases using the search term \"machine learning X-ray diffraction\" were assessed by two reviewers using a blind method. From the total number of 754 entries, 513 were identified as unique, and 11% of these were included in the current review based on several selection criteria:  
* The bibliographic source must refer to the use of machine learning methods for the analysis of XRD patterns;
* The bibliographic source must be written in English;
* The bibliographic source represents a peer-reviewed article, conference proceeding, or an edited book. The findings are presented and compared to traditional XRD data analysis methods.