Physics-Based and Data-Driven Multiscale Modeling of the Structural Formation in Macromolecular Systems
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Beschreibung
In order to improve knowledge on macromolecular structural formation and self-assembly, this work proposes a physics-based and data-driven multiscale modeling framework capable of describing structural formation on micro-meter and milli-second scales near molecular-level precision. The framework abstracts macromolecules as anisotropic unit objects and models the interactions and environment using data-driven approaches. The models are parameterized in a bottom-up fashion and validated top-down by comparison with literature and collaborator data for self-assembly of three model system: alginate gelation, hepatitis B virus capsids, and the pyruvate dehydrogenase complex.
Autor*in
Philipp Nicolas Depta
Themen in »Physics-Based and Data-Driven Multiscale Modeling of the Structural Formation in Macromolecular Systems«
multiscale modeling, molecular modeling Molecular Discrete Element Method, MDEM Discrete Element Method, DEM coarse-graining Molecular Dynamics, MD, Langevin dynamics machine learning, ML, supervised learning Kriging, macromolecular self-assembly, structural formation simulation anisotropic macromolecules, assembly pathways, assembly kinetics molecular collisions, 6D intermolecular interaction potentials specialized force-fields, molecular binding, bonded interaction hepatitis B core antigen, HBcAg, capsid formation virus-like particles, VLP, pyruvate dehydrogenase complex PDC, alginate alginic acid, biopolymer, gelation gel, aerogel, porous nanomaterial