Alam, Md. AshrafulBokul, SaffatAbdus Shukur, Samiha Sabrin MdAhmed, Saquib2019-11-042019-11-0420192019-08ID 16301001ID 16201037ID 17301181http://hdl.handle.net/10361/12825Cataloged from PDF version of thesis.Includes bibliographical references (pages 50-53).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.Machine learning is used to carry out e cient studies and analyses in the eld of condensed matter physics. We propose comprehensive machine learning approaches that would classify between magnetic structures. We propose models that are trained on data that has been generated on 3D lattices of Heisenberg model using the physical properties of respective magnetic structures. Models are designed based on three types of classi cations, rst classi cation is done between topologically-protected structures, second on non-topologically-protected structures, thirdly on all structures collectively. To achieve this, convolutional neural network (CNN) and support vector machine (SVM) with principle component analysis (PCA) algorithms have been used. We then make a comparative analysis and nd the most optimal solution. The results show that CNN provides the highest accuracy in the classi cation of topological and non-topological magnetic con gurations.53 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Convolutional Neural NetworkSupport vector machinePrinciple component analysisSkyrmionFerromagneticSpin-spiraAntiskyrmionAnti-ferromagneticTopologicalMachine learningComputer algorithmsMachine learning--Mathematical modelsThesis