Morten Ledum

• Ledum, Morten; Sen, Samiran; Li, Xinmeng; Carrer, Manuel; Feng, Yu & Cascella, Michele [Vis alle 7 forfattere av denne artikkelen] (2023). HylleraasMD: A Domain Decomposition-Based Hybrid Particle-Field Software for Multiscale Simulations of Soft Matter. Journal of Chemical Theory and Computation. ISSN 1549-9618. doi: 10.1021/acs.jctc.3c00134. Fulltekst i vitenarkiv
• Ledum, Morten; Sen, Samiran; Bore, Sigbjørn Løland & Cascella, Michele (2023). On the equivalence of the hybrid particle-field and Gaussian core models. Journal of Chemical Physics. ISSN 0021-9606. 158(19). doi: 10.1063/5.0145142. Fulltekst i vitenarkiv
• Ledum, Morten; Carrer, Manuel; Sen, Samiran; Li, Xinmeng; Cascella, Michele & Bore, Sigbjørn Løland (2023). HylleraasMD: Massively parallel hybrid particle-field molecular dynamics in Python. Journal of Open Source Software (JOSS). ISSN 2475-9066. 8(84). doi: 10.21105/joss.04149. Fulltekst i vitenarkiv
• Sen, Samiran; Ledum, Morten; Bore, Sigbjørn Løland & Cascella, Michele (2023). Soft Matter under Pressure: Pushing Particle-Field Molecular Dynamics to the Isobaric Ensemble. Journal of Chemical Information and Modeling. ISSN 1549-9596. 63, s. 2207–2217. doi: 10.1021/acs.jcim.3c00186. Fulltekst i vitenarkiv
• Ledum, Morten; Bore, Sigbjørn Løland & Cascella, Michele (2020). Automated determination of hybrid particle-field parameters by machine learning. Molecular Physics. ISSN 0026-8976. 118(19-20), s. 1–12. doi: 10.1080/00268976.2020.1785571. Fulltekst i vitenarkiv

Se alle arbeider i Cristin

• Ledum, Morten; Cascella, Michele & Gauss, Jurgen (2022). Hamiltonian hybrid particle-field method for biological soft matter: Efficient simulation and machine learning approaches. Department of Chemistry, Faculty of Mathematics and Natural Sciences, University of Oslo. ISSN 1501-7710. 2022(2516). Vis 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.

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