Another convolution arises from the variable crystallite sizes of phases found in sla…ĮVALUATING THE TEMPERATURE DEPENDENCE OF PZT STRUCTURES USING A VIRTUAL REALITY ENVIRONMENT M.A. Cook …The number of phases frequently exceeds 10, with certain slag types (EAF, BOF, blends, stainless) having extreme peak overlap, making identification difficult. When compared with classical machine learning methods, such as Support Vect…ĬHALLENGES OF QUANTITATIVE PHASE ANALYSIS OF SLAGS: A LOOK AT SAMPLE COMPLEXITY J.E. Reischl …Previous work has shown that CNNs can be trained to successfully identify crystalline phases from XRD patterns and classify their symmetry, even in multi-phase mixtures. Without a higher quality alternative, this defective structure remains as the only entry in the various crystall…Ī CRITICAL REVIEW OF NEURAL NETWORKS FOR THE IDENTIFICATION OF POWDER X-RAY DIFFRACTION PATTERNS J. Smalley …The lack of literature on Form-II characterization highlights the difficulty in producing a phase-pure sample even in a powder form.
Current Research Using the Powder Diffraction File.PreDICT with DICVOL14 Indexing (freeware).
Pharmaceutical Powder X-ray Diffraction Symposium (PPXRD).Powder Diffraction File (PDF) – Phase Search.
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