{"title":"Machine Learning Based Classification of crystal system using rendered images from X-ray diffraction (XRD) dataset","authors":"A. Hamza, Umar Hayat, Wahid Hussain, Anam Mumtaz","doi":"10.1109/ICAI58407.2023.10136622","DOIUrl":null,"url":null,"abstract":"X-ray diffraction(XRD) is an essential characterization technique to study the properties of the materials. Finding a material's crystal system is an important step in its analysis. So, the process should be fast as well as accurate. In total there are seven crystal systems: triclinic, monoclinic, orthorhombic, tetragonal, trigonal, hexagonal, and cubic. Previous studies have worked on finding the material crystal structure by introducing machine learning approaches. In, recent studies the X-ray diffraction(XRD) dataset in the tabular form was used to train a machine learning model to classify the material's crystal system. The machine learning models trained on the tabular X-ray diffraction(XRD) dataset didn't maximize their performance. In the scope of this study rendered X-ray diffraction(XRD) images had been used to maximize the performance of the machine learning models. By using the rendered images as input from the X-ray diffraction(XRD) tabular dataset the machine learning model was able to achieve an accuracy of 98%-99%. The final findings had shown that rendered images datasets had improved the machine learning model's ability to correctly classify the crystal systems of materials.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 3rd International Conference on Artificial Intelligence (ICAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAI58407.2023.10136622","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
X-ray diffraction(XRD) is an essential characterization technique to study the properties of the materials. Finding a material's crystal system is an important step in its analysis. So, the process should be fast as well as accurate. In total there are seven crystal systems: triclinic, monoclinic, orthorhombic, tetragonal, trigonal, hexagonal, and cubic. Previous studies have worked on finding the material crystal structure by introducing machine learning approaches. In, recent studies the X-ray diffraction(XRD) dataset in the tabular form was used to train a machine learning model to classify the material's crystal system. The machine learning models trained on the tabular X-ray diffraction(XRD) dataset didn't maximize their performance. In the scope of this study rendered X-ray diffraction(XRD) images had been used to maximize the performance of the machine learning models. By using the rendered images as input from the X-ray diffraction(XRD) tabular dataset the machine learning model was able to achieve an accuracy of 98%-99%. The final findings had shown that rendered images datasets had improved the machine learning model's ability to correctly classify the crystal systems of materials.