Media type: E-Book Title: The structure and dynamics of materials using machine learning Contributor: Gonçalves Marques, Mário Rui [Verfasser]; Marques, Miguel [Gutachter]; Paul, Wolfgang [Gutachter]; Rinke, Patrick [Gutachter] imprint: Halle (Saale): Universitäts- und Landesbibliothek Sachsen-Anhalt, 2020 Extent: Online-Ressource Language: English DOI: 10.25673/33597 Identifier: Keywords: 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. ; Maschinelles Lernen ; Strukturvorhersage ; Molekulardynamik ; Kraftfelder neuronaler Netze ; Defekte ; Photovoltaik ; Dichtefunktionaltheorie ; Clustererweiterung ; Cui ; CZTS ; Si ; Cu ; Au ; Machine Learning ; Structure prediction ; Molecular dynamics ; Neural Network force-fields ; Defects ; Photovoltaics ; Density functional theory ; Origination: University thesis: Dissertation, Halle (Saale), Martin-Luther-Universität Halle-Wittenberg, 2020 Footnote: Access State: Open Access