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Αρχική Συλλογές Τύπος Εργασίας Πτυχιακές εργασίες

Πτυχιακές εργασίες

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Τίτλος

Development and application of Machine Learning approaches to investigate the configurational space of binary alloys

Συγγραφέας

Γκάνας, Μάριος

Περίληψη

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.

Γλώσσα

Αγγλικά

Ημερομηνία έκδοσης

2024-07-11

Συλλογή

Σχολή/Τμήμα--Σχολή Θετικών και Τεχνολογικών Επιστημών--Τμήμα Φυσικής--Πτυχιακές εργασίες

Τύπος Εργασίας--Πτυχιακές εργασίες

Μόνιμη Σύνδεση

https://elocus.lib.uoc.gr//dlib/5/7/b/metadata-dlib-1718778117-140509-29927.tkl

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