A new formulation of configurational-bias Monte Carlo that uses arbitrary distributions to generate trial bond lengths, angles and dihedrals is described and shown to provide similar acceptance rates with substantially less computational effort. Several different trial distributions are studied and a linear combination of the ideal distribution plus Gaussian distributions automatically fit to the energetic and ideal terms is found to give the best results. The use of these arbitrary trial distributions enables a new formulation of coupled-decoupled configurational bias Monte Carlo that has significantly higher acceptance rates for cyclic molecules. The chemical potential measured via a modified Widom insertion is found to be ill-defined in the case of a molecule that has flexible bond lengths due to the unbounded probability distribution that describes the distance between any two atoms. We propose a simple standard state that allows the computation of consistent chemical potentials for molecules with flexible bonds. We show that the chemical potential via Widom insertion is not computed properly for molecules with Coulombic interactions when the number of trials for any of the nonbonded selection steps is greater than one. Finally, we demonstrate the power of the new algorithms by sampling the side-chain conformations of a polypeptide.
Carbon nanotubes (CNT) are unique nanoscale building blocks for a variety of materials and applications, from nanocomposites, sensors and molecular electronics to drug and vaccine delivery. An important step towards realizing these applications is the ability to controllably self-assemble the nanotubes into larger structures. Recently, amphiphilic peptide helices have been shown to bind to carbon nanotubes and thus solubilize them in water. Furthermore, the peptides then facilitate the assembly of the peptide-wrapped nanotubes into supramolecular, well-aligned fibers. We investigate the role that molecular modeling can play in elucidating the interactions between the peptides and the carbon nanotubes in aqueous solution. Using ab initio methods, we have studied the interactions between water and CNTs. Classical simulations can be used on larger length scales. However, it is difficult to sample in atomistic detail large biomolecules such as the amphiphilic peptide of interest here. Thus, we have explored both new sampling methods using configurational-bias Monte Carlo simulations, and also coarse-grained models for peptides described in the literature. An improved capability to model these inorganichiopolymer interfaces could be used to generate improved understanding of peptide-nanotube self-assembly, eventually leading to the engineering of new peptides for specific self-assembly goals.
Understanding the properties and behavior of biomembranes is fundamental to many biological processes and technologies. Microdomains in biomembranes or ''lipid rafts'' are now known to be an integral part of cell signaling, vesicle formation, fusion processes, protein trafficking, and viral and toxin infection processes. Understanding how microdomains form, how they depend on membrane constituents, and how they act not only has biological implications, but also will impact Sandia's effort in development of membranes that structurally adapt to their environment in a controlled manner. To provide such understanding, we created physically-based models of biomembranes. Molecular dynamics (MD) simulations and classical density functional theory (DFT) calculations using these models were applied to phenomena such as microdomain formation, membrane fusion, pattern formation, and protein insertion. Because lipid dynamics and self-organization in membranes occur on length and time scales beyond atomistic MD, we used coarse-grained models of double tail lipid molecules that spontaneously self-assemble into bilayers. DFT provided equilibrium information on membrane structure. Experimental work was performed to further help elucidate the fundamental membrane organization principles.
Classical density functional theory (DFT) is applied to study properties of fully detailed, realistic models of poly(dimethylsiloxane) liquids near silica surfaces and compared to results from molecular dynamics simulations. In solving the DFT equations, the direct correlation functions are obtained from the polymer reference interaction site model (PRISM) theory for the repulsive parts of the interatomic interactions, and the attractions are treated via the random-phase approximation (RPA). Good agreement between density profiles calculated from DFT and from the simulations is obtained with empirical scaling of the direct correlation functions. Separate scaling factors are required for the PRISM and RPA parts of the direct correlation functions. Theoretical predictions of stress profiles, normal pressure, and surface tensions are also in reasonable agreement with simulation results.
Poly(ethylene oxide) (PEO) is the quintessential biocompatible polymer. Due to its ability to form hydrogen bonds, it is soluble in water, and yet is uncharged and relatively inert. It is being investigated for use in a wide range of biomedical and biotechnical applications, including the prevention of protein adhesion (biofouling), controlled drug delivery, and tissue scaffolds. PEO has also been proposed for use in novel polymer hydrogel nanocomposites with superior mechanical properties. However, the phase behavior of PEO in water is highly anomalous and is not addressed by current theories of polymer solutions. The effective interactions between PEO and water are very concentration dependent, unlike other polymer/solvent systems, due to water-water and water-PEO hydrogen bonds. An understanding of this anomalous behavior requires a careful examination of PEO liquids and solutions on the molecular level. We performed massively parallel molecular dynamics simulations and self-consistent Polymer Reference Interaction Site Model (PRISM) calculations on PEO liquids. We also initiated MD studies on PEO/water solutions with and without an applied electric field. This work is summarized in three parts devoted to: (1) A comparison of MD simulations, theory and experiment on PEO liquids; (2) The implementation of water potentials into the LAMMPS MD code; and (3) A theoretical analysis of the effect of an applied electric field on the phase diagram of polymer solutions.
As electronic and optical components reach the micro- and nanoscales, efficient assembly and packaging require the use of adhesive bonds. This work focuses on resolving several fundamental issues in the transition from macro- to micro- to nanobonding. A primary issue is that, as bondline thicknesses decrease, knowledge of the stability and dewetting dynamics of thin adhesive films is important to obtain robust, void-free adhesive bonds. While researchers have studied dewetting dynamics of thin films of model, non-polar polymers, little experimental work has been done regarding dewetting dynamics of thin adhesive films, which exhibit much more complex behaviors. In this work, the areas of dispensing small volumes of viscous materials, capillary fluid flow, surface energetics, and wetting have all been investigated. By resolving these adhesive-bonding issues, we are allowing significantly smaller devices to be designed and fabricated. Simultaneously, we are increasing the manufacturability and reliability of these devices.