# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 4 Applications of Machine Learning in XRD Data Analysis
### Pattern Matching and Classification Algorithms
In what concerns the 23 experimentally synthesized samples, the mean values of accuracy were 0.73 for CNN and 0.78 for RF. The lower accuracy obtained for the experimentally raw patterns was explained by the authors in terms of the different effects, such as the preferential orientation and different signal/noise ratio [92; 93; 94; 95]. In geothermal fields, the classification of rock cuttings is important for understanding the geothermal system and for selecting a promising site [96]. Rock cuttings containing 24 minerals (Table 3) were obtained from two wells in the Hachimantai geothermal field, which may have formed during hydrothermal alteration according to [PERSON] et al. For the assessment of three ML algorithms, namely, K-mean clustering, Gaussian mixture model, and agglomerative clustering [96], the authors prepared a dataset of 88 simulated samples with four mineral distributions along a well down to 1000 m with a depth spacing of 10 m. The classification of the samples was performed using three labels: quartz index, temperature, and depth. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{8}{c}{**Classifier**} \\ \cline{2-9}  & **SVM** & **NB** & **KNN** & **RF** & **CNN:** & **CNN:** & **CNN:** & **CNN:** & **Polar-Max** \\  & & & & & **Cartesian** & **Polar-Min** & **Polar-Max** \\ \hline Artifact & 0.85 & 0.78 & 0.87 & 0.91 & 0.94 & 0.93 & 0.92 \\ Background Ring & 0.72 & 0.61 & 0.72 & 0.86 & 0.92 & 0.91 & 0.90 \\ Diffuse Scattering & 0.93 & 0.45 & 0.93 & 0.93 & 0.96 & 0.95 & 0.97 \\ Ice Ring & 0.14 & 0.80 & 0.93 & 0.95 & 0.99 & 0.99 & 0.98 \\ Loop Scattering & 0.70 & 0.62 & 0.71 & 0.83 & 0.94 & 0.95 & 0.96 \\ Nonuniform Detector Response & 0.45 & 0.68 & 0.75 & 0.81 & 0.87 & 0.89 & 0.89 \\ Strong Background & 0.90 & 0.87 & 0.89 & 0.93 & 0.94 & 0.91 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy of the class-specific predictive performance for the different classifier algorithms. Data from reference [92]. The K-means clustering and Gaussian mixture algorithms provided similar results, whereas the agglomerative clustering showed unique classification outcomes. The methodology proposed by the authors is applicable to other boreholes in geothermal fields. In materials science, the composition-structure-properties (CSP) paradigm is often used for predicting materials behavior under certain conditions. [PERSON] et al. [97] developed a supervised machine-learning algorithm to classify materials encountered in aviation security determinations based on CSP and XRD patterns without material identification. For this purpose, a dataset of 206 relevant materials in stream of commerce baggage (explosives, prohibited flammables, acids, plastic, metals, food, etc.) was prepared. It is worth mentioning that the dataset included both crystalline and amorphous compounds, which can easily be discerned from XRD data. The dataset was classified by crystalline/noncrystalline, solid/liquid, explosive/nonexplosive, prohibited/allowed classes with satisfactory results, as the authors state. In what concerns pattern matching and classification, ML methods offer rapid automation and complex pattern recognition in XRD data analysis, improving accuracy and adaptability. However, their effectiveness relies on the availability of labeled data, and complex models might overfit noise. Further improvement of the models might arise from using larger databases compared to those reported by the authors in our review, such as Crystallography Open Database (505,398 entries) or Powder Diffraction File (1,186,076 entries). Conventional methods lack automation and struggle with intricate patterns, but they are more interpretable and require less data.