Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/33597
Title: The structure and dynamics of materials using machine learning
Author(s): Gonçalves Marques, Mário RuiLook up in the Integrated Authority File of the German National Library
Referee(s): Marques, MiguelLook up in the Integrated Authority File of the German National Library
Paul, WolfgangLook up in the Integrated Authority File of the German National Library
Rinke, Patrick
Granting Institution: Martin-Luther-Universität Halle-Wittenberg
Issue Date: 2020
Extent: 1 Online-Ressource (135 Seiten)
Type: HochschulschriftLook up in the Integrated Authority File of the German National Library
Type: Doctoral thesis
Exam Date: 2020-05-05
Language: English
URN: urn:nbn:de:gbv:3:4-1981185920-337946
Abstract: Diese Arbeit leistet einen Beitrag zur Entwicklung und Charakterisierung neuer Materialien. Für viele Simulationen, z. B. Molekulardynamik-Simulationen, die zur Berechnung von Materialeigenschaften benutzt werden, ist es notwendig die Gesamtenergie und ihre Ableitungen tausende Male zu berechnen. Diese Zahl steigt für lange Simulationen oder große Systeme leicht in die Millionen an, was trotz effizienter Methoden wie Dichte-Funktional-Theorie extrem hohe Rechenkosten verursacht. Das Ziel dieser Arbeit ist die Entwicklung von Strategien diese Hindernisse mittels maschinellen Lernmethoden zu umgehen.
This thesis provides a contribution to the problem of material discovery and characterization. Many simulations used to predict properties of materials, such as molecular dynamics and structural prediction, require thousands of total energy calculations (and its derivatives). This number can easily grow above millions for large systems or for long simulation times, which translates to high computational costs even for methods as efficient as density functional theory (which is the standard method to perform these calculations in material science). The aim of this thesis is to develop strategies to counter these obstacles using machine learning techniques.
URI: https://opendata.uni-halle.de//handle/1981185920/33794
http://dx.doi.org/10.25673/33597
Open Access: Open access publication
License: In Copyright
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