We demonstrate that neglecting their contribution leads to qualitative discrepancies in predicted surfactant addition rates and propose a stochastic model for the monomer addition which takes the additional degrees of freedom into account. The model parameters are extracted from molecular dynamics simulations and the surfactant addition rates are determined from Brownian dynamics simulations of this model. The obtained addition and removal rates are then incorporated into the kinetic model of micelle formation and disintegration. It is expected that insights gained in the course of development of the multi-scale model for this relatively simple self-assembly process will aid in the development of models for dynamics of more complex processes in amphiphilic systems such as collision of reverse micelles involved in formation of nanoparticles, rheology of worm-like micellar solutions, and fusion of lipid bilayers.

Self-assembly and phase separation in mixtures are processes of great interest which provide large opportunities for designing novel nanomaterials. Since the aggregation behaviors are governed by noncovalent interactions and configurational entropy of molecules, computational modeling can shed light on the design principles by detecting molecular details beyond the resolution of experiments. We use molecular dynamics (MD) simulations to model bulk condensed matter systems, then inspect the topology and time evolution of the systems from atomic scales to nanometer scales for time of nanoseconds. We characterize the thermodynamic driving force of the association of ionic 'gemini' surfactants in water. We find that different headgroup electrostatics and size lead to qualitatively different intermolecular configurations of the surfactant associates, then discriminate whether the process is energetically or entropically driven. We also model low-symmetry packing phases comprised of spherical micelles of simple ionic surfactants. Here we notice the frustration of ideal packing by electrostatic interactions brings total inhomogeneity of structure and dynamics. These studies provide insight that the detailed topology and conditions of amphiphile self-assembly are hierarchically affected by chemical details of individual molecules and overall partitions of the system constituents. In another path, we utilize first-principles to develop force fields which can explicitly represent electronic polarizations as a response to environments. We demonstrate the polarization feature grants large benefits on the model transferability on hydrogen-bonding systems, regarding our applications on the urea/water mixture, and the choline chloride/urea mixture. Our new polarizable force field of urea is working well on both crystal and aqueous solution phases, and it is superior than nonpolarizable force field especially in the prediction of temperature-dependent solubility in water. Also, our choline chloride force field is combined with urea, and shows fair accuracy of predicting hydrogen bond network microstructure and self-diffusion of the bulk liquid. Thus, we conclude our studies enable comprehensive modeling of nanoscale processes while implementing chemical details with reasonable computational costs.

Discover how the latest computational tools are building our understanding of particle interactions and leading to new applications With this book as their guide, readers will gain a new appreciation of the critical role that particle interactions play in advancing research and developing new applications in the biological sciences, chemical engineering, toxicology, medicine, and manufacturing technology The book explores particles ranging in size from cations to whole cells to tissues and processed materials. A focus on recreating complex, real-world dynamical systems helps readers gain a deeper understanding of cell and tissue mechanics, theoretical aspects of multiscale modeling, and the latest applications in biology and nanotechnology. Following an introductory chapter, Multiscale Modeling of Particle Interactions is divided into two parts: Part I, Applications in Nanotechnology, covers: Multiscale modeling of nanoscale aggregation phenomena: applications in semiconductor materials processing Multiscale modeling of rare events in self-assembled systems Continuum description of atomic sheets Coulombic dragging and mechanical propelling of molecules in nanofluidic systems Molecular dynamics modeling of nanodroplets and nanoparticles Modeling the interactions between compliant microcapsules and patterned surfaces Part II, Applications in Biology, covers: Coarse-grained and multiscale simulations of lipid bilayers Stochastic approach to biochemical kinetics In silico modeling of angiogenesis at multiple scales Large-scale simulation of blood flow in microvessels Molecular to multicellular deformation during adhesion of immune cells under flow Each article was contributed by one or more leading experts and pioneers in the field. All readers, from chemists and biologists to engineers and students, will gain new insights into how the latest tools in computational science can improve our understanding of particle interactions and support the development of novel applications across the broad spectrum of disciplines in biology and nanotechnology.

Fully atomistic computer simulations of surfactant self-assembly are extremely challenging because of the different length scales and the associated different times scales, implying large system sizes and tediously long simulations. To overcome this, the uninteresting degrees of freedom at the atomistic level can be integrated out leading to a meso-scale model, which can span the required length and time scales with less computational burden. We use such a meso-scale model to study surfactant self-assembly and how alcohols affect this self-assembly behavior in supercritical carbon dioxide. Here the surfactants and alcohols are represented as a chain of beads where each bead represents a set of atoms. This model is implemented into lattice Monte Carlo simulations. We show that short chain alcohols act as cosurfactants by concentrating in the surfactant layer of the aggregates, strongly decreasing micellar size and increasing the number of aggregates. In contrast long chain alcohols act as cosolvents by concentrating more in the solvent and increasing the micellar size. We then focus on systematically constructing a meso-scale model that preserves the important aspects of the atomistic model, while spanning these different length and time scales. The process of constructing this meso-scale model from the corresponding atomistic model is called coarse-graining. We first explore the rigorous coarse-graining technique in which we match the partition function of the atomistic model with that of the meso-scale model. Such a rigorous procedure has the advantage that it leads to the reproduction of all the structural and thermodynamic properties of the atomistic model in the meso-scale model. We develop a procedure to calculate the rigorous 1, 2 ... N-body effective interactions using Widom's particle insertion method. We implement this rigorous procedure for a binary ArD r system, where the degrees of freedom of Ar are integrated out. We observed that the structure at.

Provides comprehensive knowledge on concepts, theoretical methods and state-of-the-art computational techniques for the simulation of self-assembling systems Looks at the field of self-assembly from a theoretical perspective Highlights the importance of theoretical studies and tailored computer simulations to support the design of new self-assembling materials with useful properties Divided into three parts covering the basic principles of self-assembly, methodology, and emerging topics

Keywords: coarse-graining, cosolvent, supercritical, alcohol, co2, cosurfactant, multi-scale, simulation, modeling, surfactant, self-assembly, effective potential.

While the relevant features and properties of nanosystems necessarily depend on nanoscopic details, their performance resides in the macroscopic world. To rationally develop and accurately predict performance of these systems we must tackle problems where multiple length and time scales are coupled. Rather than forcing a single modeling approach to