[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122188-en":3,"doc-seo-122188-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122188,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Exploring Artificial Spin Ice Through Machine Learning Analysis","Artificial spin ice (ASI) is studied for its ability to exhibit topological defects, magnetic monopoles, and complex spin textures arising from geometric frustration. Its controllability makes ASI promising for data storage, microwave filtering, and spintronics, while also serving as model systems for frustrated magnetic materials. This thesis evaluates the accuracy and effectiveness of a restricted Boltzmann machine (RBM) machine-learning model on ASI data. Using RBM for interpretability in statistical physics, the work analyzes defects’ behaviors and characteristic features. Results inform how to optimize ASI for targeted functionalities through improved understanding of magnetic interactions and anisotropy.","Exploring Artificial Spin Ice Through Machine  \nLearning Analysis  \nby  \nMahdis Hamdi  \nA thesis submitted to The Faculty of Graduate Studies of The University of Manitoba  \nin partial fulfillment of the requirements of the degree of  \nMaster of Science  \nDepartment of Physics and Astronomy  \nThe University of Manitoba  \nWinnipeg, Manitoba, Canada  \nNovember 2023  \n© Copyright 2023 by Mahdis Hamdi  \nThesis advisor Author  \nRobert Stamps Mahdis Hamdi  \nExploring Artificial Spin Ice Through Machine Learning  \nAnalysis  \nAbstract  \nArtificial spin ice (ASI) systems have emerged as a captivating research field due to their ability to exhibit intriguing properties, including topological defects, magnetic monopoles, and complex spin textures. These systems offer a versatile platform for studying geometric frustration and exploring emergent magnetic phenomena. Moreover, the controllable nature of ASIs holds promise for potential applications in data storage, microwave filtering, and spintronics. ASI also serve as valuable model systems for understanding the physics of magnetic materials and frustrated systems.  \nThis thesis focuses on investigating the accuracy of a machine learning model on ASI materials using the framework of the restricted Boltzmann machine (RBM) . RBM offers interpretability within the realm of statistical physics and can effectively analyse and interpret the vast amount of data generated in condensed matter physics experiments and simulations.  \nBy leveraging RBM, we aim to gain deeper insights into the behaviour and characteristics of defects in ASI systems. Defects possess unique properties and dynamics that hold potential for information storage, logic operations, and magnetic texture manipulation. Understanding and engineering these defects allow for precise control  \nover the magnetic properties of ASI, including interactions and anisotropy, enabling tailored functionalities for specific applications.  \nThis research explores the accuracy and effectiveness of RBM models specifically applied to ASI systems. By utilizing RBM, we aim to analyse and uncover the intricate behaviours and features of defects in ASI, contributing to a deeper understanding of these materials and their potential applications.  \nThe findings from this study provide valuable insights into the behaviour of defects in ASI materials and offer avenues for optimizing these systems for desired functionalities. By leveraging machine learning techniques, such as RBM, we can further explore and harness the potential of ASI systems for future advancements in magnetic materials and condensed matter physics.  \nContents  \nAbstract ..................................... ii  \nTable of Contents ................................ v  \nList [of Figures .................................. vi](of Figures .................................. vi)  \n[Acknowledgments ................................ ix](Acknowledgments ................................ ix)  \n[Research Tools ................................. x](Research Tools ................................. x)  \n1 Introduction 1  \n1.1 Background: Artificial spin ice ...................... 1  \n1.2 Background: Machine Learning ..................... 3  \n1.3 Background: Artificial Neural Network ................. 5  \n1.4 Background: Restricted Boltzmann Machine (RBM) .......... 6  \n1.5 Objective ................................. 8  \n1.6 Overview of this thesis .......................... 9  \n2 Artificial Spin Ice 11  \n2.1 Geometrical Frustration ......................... 11  \n2.2 Water ice and Spin ice .......................... 12  \n2.3 Artificial Spin Ice ............................. 14  \n2.4 Model of interactions ........................... 18  \n2.5 Monte Carlo Method ........................... 19  \n2.6 Vertical Elements ............................. 22  \n3 Restricted Boltzmann Machine 24  \n3.0.1 Structure ............................. 24  \n3.0.2 Training In RBM ......................... 30  \nGibbs Sampling ........","cbCaihWhD2x4PMLH","https://ap.wps.com/l/cbCaihWhD2x4PMLH","pdf",12016642,1,85,"English","en",105,"# Abstract\n# Table of Contents\n## List of Figures\n## Acknowledgments\n## Research Tools\n# 1 Introduction\n## 1.1 Background: Artificial spin ice\n## 1.2 Background: Machine Learning\n## 1.3 Background: Artificial Neural Network\n## 1.4 Background: Restricted Boltzmann Machine (RBM)\n## 1.5 Objective\n## 1.6 Overview of this thesis\n# 2 Artificial Spin Ice\n## 2.1 Geometrical Frustration\n## 2.2 Water ice and Spin ice\n## 2.3 Artificial Spin Ice\n## 2.4 Model of interactions\n## 2.5 Monte Carlo Method\n## 2.6 Vertical Elements\n# 3 Restricted Boltzmann Machine\n## 3.0.1 Structure\n## 3.0.2 Training In RBM\n## 3.0.3 The Algorithm\n## 3.0.4 Examining Algorithm Effectiveness\n## 3.0.5 Analyzing RBM Results for the Ising Model\n# 4 Utilizing RBM for Artificial Spin Ice Data\n## 4.1 Initialize the system\n## 4.2 Characterizing the data and representing the results\n## 4.3 Evaluation\n## 4.4 Adding Vertical Elements\n# 5 Conclusion\n## 5.1 Summery and Results\n## 5.2 Future Work\n# A Supporting Data\n# Bibliography","[{\"question\":\"What research problem does the thesis address?\",\"answer\":\"The thesis investigates how accurately and effectively a restricted Boltzmann machine (RBM) model can analyze artificial spin ice (ASI) materials, with an emphasis on defect behavior and features.\"},{\"question\":\"Why is RBM suitable for analyzing ASI data?\",\"answer\":\"RBM provides interpretability within statistical physics and can analyze the large datasets produced by condensed matter experiments and simulations, making it useful for extracting meaningful defect characteristics.\"},{\"question\":\"What applications motivate studying defects in ASI systems?\",\"answer\":\"The work notes that defects’ unique properties and dynamics could support information storage, logic operations, and magnetic texture manipulation through controlled magnetic interactions and anisotropy.\"}]","Exploring Artificial Spin Ice Through Machine Learning Analysis | 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research problem does the thesis address?","Question",{"text":75,"@type":76},"The thesis investigates how accurately and effectively a restricted Boltzmann machine (RBM) model can analyze artificial spin ice (ASI) materials, with an emphasis on defect behavior and features.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is RBM suitable for analyzing ASI data?",{"text":80,"@type":76},"RBM provides interpretability within statistical physics and can analyze the large datasets produced by condensed matter experiments and simulations, making it useful for extracting meaningful defect characteristics.",{"name":82,"@type":73,"acceptedAnswer":83},"What applications motivate studying defects in ASI systems?",{"text":84,"@type":76},"The work notes that defects’ unique properties and dynamics could support information storage, logic operations, and magnetic texture manipulation through controlled magnetic interactions and 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