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Title Development and application of Machine Learning approaches to investigate the configurational space of binary alloys
Author Γκάνας, Μάριος
Abstract In this thesis, a novel methodology has been developed that allows for the identification and analysis of configurational patterns in alloys. This meth- odology explicitly incorporates the symmetry properties of the parent lattice and is based on unsupervised machine learning approaches. More specifically, unit cells of various sizes and symmetries are used to describe the configura- tions. These configurations are represented by vectors with lengths equal to the number of atoms enclosed by the unit cells. To search for patterns and dominant configurations within the thousands of vectors, a self-consistent clustering algorithm has been developed. By applying this approach, cluster centers are constructed, and the representation vectors are assigned to these cluster centers. Moreover, a degree of order parameter is defined, allowing the assignment of the highest symmetry cluster center to each lattice site. The aforementioned methodology is applied to pseudobinary InGaN alloys. As input, large alloy structures consisting of more than 105 atoms, produced by Monte Carlo calculations, are used. The results confirm the tendency of In atoms to align as second nearest neighbors in InGaN, leading to √3 × √3 translational symmetry. The outcome of the developed methodology, i.e., cluster centers, can be directly used in density functional theory calculations or to produce special quasirandom structures with modified probabilities for the occupation of the lattice sites.
Language English
Issue date 2024-07-11
Collection   School/Department--School of Sciences and Engineering--Department of Physics--Graduate theses
  Type of Work--Graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/5/7/b/metadata-dlib-1718778117-140509-29927.tkl Bookmark and Share
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