We apply the dissipative particle dynamics strategy proposed by Hijón et al. [Faraday Discuss. 144, 301-322 (2010)] and based on an exact derivation of the generalized Langevin equation to cis- and trans-1,4-polybutadiene. We prove that it is able to reproduce not only the structural but also the dynamical properties of these polymers without any fitting parameter. A systematic study of the effect of the level of coarse-graining is done on cis-1,4-polybutadiene. We show that as the level of coarse-graining increases, the dynamical properties are better and better reproduced while the structural properties deviate more and more from those calculated in molecular dynamics (MD) simulations. We suggest two reasons for this behavior: the Markovian approximation is better satisfied as the level of coarse-graining increases, while the pair-wise approximation neglects important contributions due to the relative orientation of the beads at large levels of coarse-graining. Finally, we highlight a possible limit of the Markovian approximation: the fact that in constrained simulations, in which the centers-of-mass of the beads are kept constant, the bead rotational dynamics become extremely slow.
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http://dx.doi.org/10.1063/1.4975652 | DOI Listing |
Natl Sci Rev
January 2025
School of Systems Science, Beijing Normal University, Beijing 100875, China.
Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the fact that emergent behaviors cannot be directly captured by micro-level observational data. Thus, it is crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE.
View Article and Find Full Text PDFInt J Pharm
December 2024
State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China; Faculty of Health Sciences, University of Macau, Macau 999078, China. Electronic address:
Messenger RNA (mRNA) encapsulated in lipid nanoparticles (LNPs) represents a cutting-edge delivery technology that played a pivotal role during the COVID-19 pandemic and in advancing vaccine development. However, molecular structure of mRNA-LNPs at real size remains poorly understood, with conflicting results from various experimental studies. In this study, we aim to explore the assembly process and structural characteristics of mRNA-LNPs at realistic sizes using coarse-grained molecular dynamic simulations.
View Article and Find Full Text PDFCrit Care
December 2024
Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Background: Entropy quantifies the level of disorder within a system. Low entropy reflects increased rigidity of homeostatic feedback systems possibly reflecting failure of protective physiological mechanisms like cerebral autoregulation. In traumatic brain injury (TBI), low entropy of heart rate and intracranial pressure (ICP) predict unfavorable outcome.
View Article and Find Full Text PDFJ Chem Theory Comput
December 2024
University of Rostock, Institute of Physics, Albert-Einstein-Str. 23-24, D-18059 Rostock, Germany.
Investigating the molecular structure of soil organic matter (SOM), along with its intramolecular interactions and interactions with other soil components and xenobiotics, is essential due to its ecological importance. However, the complexity and heterogeneity of SOM present significant challenges for systematic studies. While experimental methods are commonly employed, atomistic simulations provide a complementary approach to exploring molecular-level processes.
View Article and Find Full Text PDFJ Chem Theory Comput
December 2024
Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output while removing the degrees of freedom that are less relevant. This reduction in model complexity allows coarse-grained molecular simulations to reach increased spatial and temporal scales compared with corresponding all-atom models. A core challenge in coarse-graining is to construct a force field that represents the interactions in the new representation in a way that preserves the atomistic-level properties.
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