9. Conclusion and Ongoing Developments
Over the past two decades, laboratory X-ray powder diffraction has evolved significantly, both technologically (sources, detectors) and methodologically (Rietveld). The speed of measurements, signal quality, and the potential for automated data processing have opened up new possibilities, such as in situ measurements (where the sample’s environment—pressure, humidity, or temperature—is modified), and operando measurements, in which the material is used in the context of its final application (cyclic voltammetry, charge/discharge testing for a battery, etc.). Large volumes of data are collected, driving the development of algorithms for automatic data processing. Numerous developments are currently underway. It is clear that these rapid changes will continue, particularly with the increasingly widespread use of Machine Learning (ML). There are two types of ML.
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