Exploring artificial spin ice through machine learning analysis
| dc.contributor.author | Hamdi, Mahdis | |
| dc.contributor.examiningcommittee | Pistorius, Stephen (Physics and Astronomy) | |
| dc.contributor.examiningcommittee | van Lierop, Johan (Physics and Astronomy) | |
| dc.contributor.supervisor | Stamps, Robert | |
| dc.date.accessioned | 2023-11-23T21:53:35Z | |
| dc.date.available | 2023-11-23T21:53:35Z | |
| dc.date.issued | 2023-11-23 | |
| dc.date.submitted | 2023-11-23T18:04:40Z | en_US |
| dc.degree.discipline | Physics and Astronomy | en_US |
| dc.degree.level | Master of Science (M.Sc.) | |
| dc.description.abstract | Artificial 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. ASIs also serve as valuable model systems for understanding the physics of magnetic materials and frustrated systems. This 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. By 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 over the magnetic properties of ASI, including interactions and anisotropy, enabling tailored functionalities for specific applications. This 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. The 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. | |
| dc.description.note | February 2024 | |
| dc.identifier.uri | http://hdl.handle.net/1993/37812 | |
| dc.language.iso | eng | |
| dc.rights | open access | en_US |
| dc.subject | Magnetism | |
| dc.subject | Artificial Spin Ice | |
| dc.subject | Machine Learning | |
| dc.subject | Restricted Boltzmann Machine | |
| dc.title | Exploring artificial spin ice through machine learning analysis | |
| dc.type | master thesis | en_US |
| local.subject.manitoba | no |