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Title Parameterizing molecular models via gaussian approximation methods
Alternative Title Ανάπτυξη αδροποιημένων μοριακών μοντέλων με μεθόδους Gaussian παλινδρόμησης
Author Κυριέρη, Βασιλική
Thesis advisor Χαρμανδάρης , Ευάγγελος
Καλλιγιαννάκη, Ευαγγελία
Reviewer Μακράκης, Γεώργιος
Πανταζής, Γιάννης
Abstract In this thesis, we study Molecular Dynamics simulations, a scientific field that focuses on the physical motion of atoms on a very small order of magnitude. Today, many chemical and biological processes, such as protein interactions, can be modeled through MD simulations with aim of analyzing their properties. The challenging part of studying MD simulations of complex systems refers to an as-accurate-as-possible prediction of the structure-property relationship at the microscopic level and the expensive calculations of the dynamic quantities due to the wide range of length and time scales. By decreasing the number of degrees of freedom, the new system can be used with fewer variables. This method, known as Coarse-Graining, maps the atomistic particles into mesoscopic particles such as ”superatoms”. There exists a variety of methods to obtain the total force of the mesoscopic system, either parametric or non-parametric models. In this thesis, the Gaussian process regression model, a flexible non-parametric family of models capable of approximating functions using relatively small data sets, is applied to a simple system, a methane system, and its results are compared with two more straightforward approximations. One with a parametric pair potential, the Lennard-Jones potential, and another with the Linear B-splines representation. We learn the approximate force fields, with the Force Matching criterion of loss, which minimizes the average distance between the atomistic forces and the approximate CG forces.
Language English
Subject Machine learning
Μηχανική μάθηση
Μοντελοποίηση μοριακών συστημάτων
Issue date 2023-03-17
Collection   School/Department--School of Sciences and Engineering--Department of Mathematics and Applied Mathematics--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/a/a/4/metadata-dlib-1679571326-442379-32146.tkl Bookmark and Share
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