An important complication of the patterns analysis is imposed by the fact that a realistic sample contains multiple phases, their patterns forming the measured spectrum together. The correspondence between the measured and the reference patterns is quantified by the likelihood function, which takes into account a model (probability distribution) of the possible experimental inaccuracies. The a priori information, if available, can be introduced by modifying the prior probabilities of different phases or chemical elements. The approach is schematically shown in Figure 1. We found physically grounded expressions for FOM calculation on the basis of Bayes’ rule, which has already been accepted as a powerful approach for measured data patterns analysis. The research, conducted by Atomicus team, enabled us to give positive answers to both questions. Can the search and match strategy be formulated in the way, suitable for modification and adaptation without applying to scientists' intuition and experience? Can the phase identification procedure be derived from certain basic physical assumptions, rather than stated in its final form? If one is trying to design an approach suitable for fully automatic powder phase identification, a number of questions arise. Such errors may hinder the correct identification of phases even when they are relatively small. Even more difficulties are encountered, when systematic errors, affecting multiple peaks in a correlated and reproducible way, are present.
#POWDER XRD ANALYSIS MANUAL#
This approach, being quite natural as an algorithmization of manual search technique, becomes less clear when modifications and adaptation for problems, not supported by our intuition, are required (accounting for coinciding peaks of several phases, “additive” search, solid solutions, etc.). ) is to combine several similarity criteria (correspondence of peaks positions, intensities, etc.) so that candidates ranking is performed according to certain requirements. Standard strategy of FOM calculation (see e.g.
The best candidate phases are chosen according to some measure of their similarity to the measured data and quantified by a figure of merit (FOM). This procedure is commonly performed by matching the measured pattern with a reference database. X-ray diffraction represents a powerful tool for identification of unknown powder phases. Artificial intelligence for automatic phase identification in powder X-ray diffraction
0 kommentar(er)
