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Graph Neural Networks for glassy materials

Francesco Saverio Pezzicoli

Graph Neural Networks for glassy materials.

Rel. Andrea Antonio Gamba, François Landes. Politecnico di Torino, Corso di laurea magistrale in Physics Of Complex Systems (Fisica Dei Sistemi Complessi), 2021

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In fundamental physics, a crucial and unsolved problem is that of understanding the behavior of glassy liquids. In these materials the viscosity or any other characteristic time increases very quickly (about 13 orders of magnitude) when the temperature is varied by only a few tens of percent around a characteristic temperature T_g, without any obvious change in the geometrical structure of their elementary constituents. This raises the question: is structure important to glassy dynamics? Several studies have shown that a lot of information about the dynamical behaviour of these materials is contained into the static structure, but how to extract this information is still an open question. A recent branch of research focuses on applying machine learning (ML) methods to extract information from static structure. Here we pursue this direction by combining a powerful family of ML models, Graph Neural Networks, with expert knowledge in the field to reach better understanding of these materials and develop new ML tools.

Relators: Andrea Antonio Gamba, François Landes
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 41
Corso di laurea: Corso di laurea magistrale in Physics Of Complex Systems (Fisica Dei Sistemi Complessi)
Classe di laurea: New organization > Master science > LM-44 - MATHEMATICAL MODELLING FOR ENGINEERING
Aziende collaboratrici: INRIA
URI: http://webthesis.biblio.polito.it/id/eprint/20440
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