dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Dijkstra, M. | |
dc.contributor.author | Delaporte, C.F.M. | |
dc.date.accessioned | 2020-03-18T19:00:55Z | |
dc.date.available | 2020-03-18T19:00:55Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/35479 | |
dc.description.abstract | Crystalline structure identification is a widely studied topic in condensed matter
physics. Understanding how the structure of materials are determined by the shape
of the particles it is build from is important for the understanding of the properties
of these materials and can potentially help us tweak these properties to develop useful
applications. In this research, we investigate how we can use machine learning tools for
the identification of crystal structures by looking at systems of rounded polyhera. The
bond order parameters of these systems are obtained from Floppy Box Monte Carlo
simulations that produce large, high dimensional data sets. This data is analyzed with
use of unsupervised machine learning, specifically dimensionality reduction techniques
PCA and diffusion maps. We find that these techniques can identify different crystal
structures for simple systems. For more complicated ones, a clear distinction of all crystal structures is hard to make with the naked eye. Additional techniques, like clustering
algorithms, are needed to make a complete identification in more complex systems. Although the differences in results are minimal, diffusion maps reduces dimensionality
slighlty better than PCA. | |
dc.description.sponsorship | Utrecht University | |
dc.format.extent | 45266882 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.title | Crystal phase identification of“odd shaped” particles with increasing roundness using machine learning. | |
dc.type.content | Bachelor Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | crystal structures, polyhedra, rounded polyhedra, machine learning, principal component analysis, PCA, diffusion maps, | |
dc.subject.courseuu | Natuur- en Sterrenkunde | |