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Using Support Vector Machines to Enhance the Performance of X-Ray Diffraction Data Analysis in Crystalline Materials Cubic Structure Identification


Mohammad Syukur


Vol. 7  No. 7  pp. 194-199


Crystalline materials cubic structure identification is very important in crystallography and material science research. For a long time researchers in the field have used manual approach in matching the result data from X-Ray Diffraction (XRD) method with the known fingerprint. These manual matching processes are complicated and sometimes are tedious because the diffracted data are complex and may have more than one fingerprint inside. This paper proposes the use of support vector machines to enhance the performance of the matching process between the diffracted data of crystalline material and the fingerprints. It is demonstrated, through experiments, that support vector machines gives more accurate and reliable identification results compared to the use of neural network.


Support Vector Machine, X-Ray Diffraction Data Analysis, Cubic Structure Identification, Material Sciences, Artificial Intelligence Application