AI-enhanced X-ray diffraction analysis: towards real-time mineral phase identification and quantification.

Area of Science:

• Materials Science
• Computational Chemistry
• Geology

Background:

• X-ray diffraction (XRD) is a crucial technique for mineral identification and quantification.
• Traditional XRD analysis can be time-consuming, limiting its application in high-throughput scenarios.

Purpose of the Study:

• To investigate the potential of convolutional neural networks (CNNs) to accelerate XRD analysis.
• To evaluate the accuracy of CNN-based XRD analysis compared to conventional methods.

Main Methods:

• Development and application of CNN models for XRD pattern processing.
• Testing the models on both synthetic and real-world mineral mixture datasets.

Main Results:

• CNNs significantly reduce the processing time required for XRD analysis.
• The developed methods demonstrate a promising level of accuracy in analyzing mineral mixtures.

Conclusions:

• Convolutional neural networks offer a revolutionary approach to XRD analysis, enhancing speed and efficiency.
• Further research is warranted to fully establish the capabilities and limitations of CNNs in quantitative XRD analysis.