Cristin-resultat-ID: 2042451
Sist endret: 11. august 2022, 13:51
Resultat
Doktorgradsavhandling
2022

Hamiltonian hybrid particle-field method for biological soft matter: Efficient simulation and machine learning approaches

Bidragsytere:
  • Morten Ledum

Utgiver/serie

Utgiver

Department of Chemistry, Faculty of Mathematics and Natural Sciences, University of Oslo

Serie

Series of dissertations submitted to the Faculty of Mathematics and Natural Sciences, University of Oslo.
ISSN 1501-7710
NVI-nivå 0

Om resultatet

Doktorgradsavhandling
Publiseringsår: 2022
Volum: 2022
Hefte: 2516

Klassifisering

Fagfelt (NPI)

Fagfelt: Kjemi og materialteknologi
- Fagområde: Realfag og teknologi

Beskrivelse Beskrivelse

Tittel

Hamiltonian hybrid particle-field method for biological soft matter: Efficient simulation and machine learning approaches

Sammendrag

Molecular dynamics describes a simulation strategy wherein systems of atoms and molecules interact, subject to known laws of classical physics. The analysis of the resulting molecular trajectories yields an incredibly powerful computational microscope with atomic resolution, providing a cost-effective alternative to chemical experiments. Although powerful, these techniques are limited by the number of atoms which can be represented simultaneously. Most biologically interesting systems contain billions of individual atoms making up e.g. cells, organelles, viruses, bacteria, or other microorganisms. In order to model such systems, it is necessary to simplify the description. The simplification treated in this thesis involves decoupling separate molecules, allowing interaction only through a slowly varying density-field. In principle this allows further increases in the system size-reaching biologically relevant soft-matter systems at the mesoscale-while retaining molecular resolution. My work has helped extend this hybrid particle-field model with the development of open-source simulation software, the addition of pressure control schemes, and a novel approach for determining the necessary model parameters by machine learning. Additionally, some theoretical results on the intrinsic approximations made by the model are considered. The research contained in this dissertation provides important steps forward towards truly realistic model representations of soft-matter systems.

Bidragsytere

Morten Ledum

  • Tilknyttet:
    Forfatter
    ved Hylleraas-senteret ved Universitetet i Oslo

Michele Cascella

  • Tilknyttet:
    Veileder
    ved Teoretisk kjemi ved Universitetet i Oslo

Jurgen Gauss

  • Tilknyttet:
    Veileder
    ved Johannes Gutenberg-Universität Mainz
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