# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 5 Conclusions and Future Development  
The use of machine learning (ML) for processing X-ray diffraction (XRD) measurements has been increasing at an accelerating rate over the last decade, as computers have become more powerful, and both ML and XRD have been streamlined and enhanced. Based on current trends, it seems that ML will continue to be harnessed for XRD data analysis, and the technique will continue to be expanded and improved.  
Future research is likely to focus on developing ML models that incorporate domain knowledge and physical constraints into the learning process. By integrating the fundamental principles of crystallography and materials science, the predictions of ML models can be more meaningful and in line with known physical property values. Alternatively, ML can be used in combination with physics-based models to obtain more interpretable, accurate, and physically meaningful predictions [110].  
Quantum mechanical methods (such as density functional theory, DFT) combined with ML is a promising frontier in materials science, which can provide highly accurate and detailed insights into the properties and electronic structure of materials. Hybrid models integrating DTF calculations with ML can accelerate material discovery by increasing the efficiency and accuracy of material property predictions [111].  
In operando and in situ studies, XRD patterns are acquired continuously during reaction or phase transformations. Real-time data analysis will facilitate and accelerate decision making and feedback, allowing researchers to monitor and control processes as they unfold, ultimately leading to deeper insights into material behavior under a wide range of conditions [107].  
In our opinion, currently, machine learning is most helpful when used in conjunction with established mathematical models and domain knowledge. Hybrid approaches benefit both from the speed and flexibility that ML techniques can offer and from the rigors of physics informed analysis that guarantee the validity of the results up to the limitations of current scientific knowledge. XRD data analysis needs new, faster methods, but they should only be adopted if they can ensure the accuracy of their results.  
Linking ML with combinatorial material analysis and high-throughput screening techniques can accelerate the discovery of novel materials with tailored properties: automated ML models can analyze vast libraries of XRD patterns to identify promising materials and suggest targeted experimental designs for further investigation, with huge potential for applications in fields like catalysis, energy materials, and drug development.  
**Author Contributions:** Conceptualization, V.-A.S.; methodology, V.-A.S.; formal analysis, V.-A.S. and R.G.; visualization, V.-A.S., writing--original draft preparation, V.-A.S. and R.G.; writing--review and editing, V.-A.S. and R.G.; supervision, V.-A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.