Simple Simulation Model for Exploring the Effects of Solvent and Structure on Asphaltene Aggregation.

Asphaltenes are operationally defined as the fraction of crude oil that is soluble in toluene but insoluble in n-heptane. According to the Yen-Mullins model, typical asphaltenes are relatively small molecules consisting of a single aromatic core flanked by aliphatic chains. The Yen-Mullins model posits that asphaltene aggregation proceeds via a hierarchical mechanism involving small nanoaggregates with stacked aromatic cores surrounded by a corona of aliphatic tails. In this work, we introduce a coarse-grained (CG) model for investigating the physical picture underlying the Yen-Mullins model and, more generally, the effects of the solvent character and molecular structure upon asphaltene self-assembly. By representing proposed asphaltenes in united atom detail, this CG model accurately describes their shape and conformational properties. Conversely, the CG model mimics varying solvent conditions by modulating the effective attraction between aliphatic and aromatic groups. Given the simplicity of this model, we performed long, replicate simulations of 147 different asphaltene solutions. As proposed by the Yen-Mullins model, island-type molecules readily form stacked aggregates under conditions that promote aromatic interactions. Interestingly, the onset of nanoaggregation appears to be insensitive to the aliphatic tails, although these tails may sterically stunt further growth of nanoaggregates. Consequently, nanoaggregates form more readily and grow larger under conditions that promote both aliphatic and aromatic interactions. In contrast, archipelago-type molecules also form large aggregates, but they do not demonstrate significant stacking interactions. Thus, the CG model reasonably describes the physical intuition of the Yen-Mullins picture and may prove to be useful for exploring later stages of asphaltene aggregation.

BOCS: Bottom-up Open-source Coarse-graining Software.

We present the BOCS toolkit as a suite of open source software tools for parametrizing bottom-up coarse-grained (CG) models to accurately reproduce structural and thermodynamic properties of high-resolution models. The BOCS toolkit complements available software packages by providing robust implementations of both the multiscale coarse-graining (MS-CG) force-matching method and also the generalized-Yvon-Born-Green (g-YBG) method. The g-YBG method allows one to analyze and to calculate MS-CG potentials in terms of structural correlations. Additionally, the BOCS toolkit implements an extended ensemble framework for optimizing the transferability of bottom-up potentials, as well as a self-consistent pressure-matching method for accurately modeling the pressure equation of state for homogeneous systems. We illustrate these capabilities by parametrizing transferable potentials for CG models that accurately model the structure, pressure, and compressibility of liquid alkane systems and by quantifying the role of many-body correlations in determining the calculated pair potential for a one-site CG model of liquid methanol.

Van der Waals Perspective on Coarse-Graining: Progress toward Solving Representability and Transferability Problems.

Low-resolution coarse-grained (CG) models provide the necessary efficiency for simulating phenomena that are inaccessible to more detailed models. However, in order to realize their considerable promise, CG models must accurately describe the relevant physical forces and provide useful predictions. By formally integrating out the unnecessary details from an all-atom (AA) model, "bottom-up" approaches can, at least in principle, quantitatively reproduce the structural and thermodynamic properties of the AA model that are observable at the CG resolution. In practice, though, bottom-up approaches only approximate this "exact coarse-graining" procedure. The resulting models typically reproduce the intermolecular structure of AA models at a single thermodynamic state point but often describe other state points less accurately and, moreover, tend to provide a poor description of thermodynamic properties. These two limitations have been coined the "transferability" and "representability" problems, respectively. Perhaps, the simplest and most commonly discussed manifestation of the representability problem regards the tendency of structure-based CG models to dramatically overestimate the pressure. Furthermore, when these models are adjusted to reproduce the pressure, they provide a poor description of the compressibility. More generally, it is sometimes suggested that CG models are fundamentally incapable of reproducing both structural and thermodynamic properties. After all, there is no such thing as a "free lunch"; any significant gain in computational efficiency should come at the cost of significant model limitations. At least in the case of structural and thermodynamic properties, though, we optimistically propose that this may be a false dichotomy. Accordingly, we have recently re-examined the "exact coarse-graining" procedure and investigated the intrinsic consequences of representing an AA model in reduced resolution. These studies clarify the origin and inter-relationship of representability and transferability problems. Both arise as consequences of transferring thermodynamic information from the high resolution configuration space and encoding this information into the many-body potential of mean force (PMF), that is, the potential that emerges from an exact coarse-graining procedure. At least in principle, both representability and transferability problems can be resolved by properly addressing this thermodynamic information. In particular, we have demonstrated that "pressure-matching" provides a practical and rigorous means for addressing the density dependence of the PMF. The resulting bottom-up models accurately reproduce the structure, equilibrium density, compressibility, and pressure equation of state for AA models of molecular liquids. Additionally, we have extended this approach to develop transferable potentials that provide similar accuracy for heptane-toluene mixtures. Moreover, these potentials provide predictive accuracy for modeling concentrations that were not considered in their parametrization. More generally, this work suggests a "van der Waals" perspective on coarse-graining, in which conventional structure-based methods accurately describe the configuration dependence of the PMF, while independent variational principles infer the thermodynamic information that is necessary to resolve representability and transferability problems.

Bottom-up coarse-grained models with predictive accuracy and transferability for both structural and thermodynamic properties of heptane-toluene mixtures.

This work investigates the promise of a "bottom-up" extended ensemble framework for developing coarse-grained (CG) models that provide predictive accuracy and transferability for describing both structural and thermodynamic properties. We employ a force-matching variational principle to determine system-independent, i.e., transferable, interaction potentials that optimally model the interactions in five distinct heptane-toluene mixtures. Similarly, we employ a self-consistent pressure-matching approach to determine a system-specific pressure correction for each mixture. The resulting CG potentials accurately reproduce the site-site rdfs, the volume fluctuations, and the pressure equations of state that are determined by all-atom (AA) models for the five mixtures. Furthermore, we demonstrate that these CG potentials provide similar accuracy for additional heptane-toluene mixtures that were not included their parameterization. Surprisingly, the extended ensemble approach improves not only the transferability but also the accuracy of the calculated potentials. Additionally, we observe that the required pressure corrections strongly correlate with the intermolecular cohesion of the system-specific CG potentials. Moreover, this cohesion correlates with the relative "structure" within the corresponding mapped AA ensemble. Finally, the appendix demonstrates that the self-consistent pressure-matching approach corresponds to minimizing an appropriate relative entropy.

Bottom-up coarse-grained models that accurately describe the structure, pressure, and compressibility of molecular liquids.

The present work investigates the capability of bottom-up coarse-graining (CG) methods for accurately modeling both structural and thermodynamic properties of all-atom (AA) models for molecular liquids. In particular, we consider 1, 2, and 3-site CG models for heptane, as well as 1 and 3-site CG models for toluene. For each model, we employ the multiscale coarse-graining method to determine interaction potentials that optimally approximate the configuration dependence of the many-body potential of mean force (PMF). We employ a previously developed "pressure-matching" variational principle to determine a volume-dependent contribution to the potential, UV(V), that approximates the volume-dependence of the PMF. We demonstrate that the resulting CG models describe AA density fluctuations with qualitative, but not quantitative, accuracy. Accordingly, we develop a self-consistent approach for further optimizing UV, such that the CG models accurately reproduce the equilibrium density, compressibility, and average pressure of the AA models, although the CG models still significantly underestimate the atomic pressure fluctuations. Additionally, by comparing this array of models that accurately describe the structure and thermodynamic pressure of heptane and toluene at a range of different resolutions, we investigate the impact of bottom-up coarse-graining upon thermodynamic properties. In particular, we demonstrate that UV accounts for the reduced cohesion in the CG models. Finally, we observe that bottom-up coarse-graining introduces subtle correlations between the resolution, the cohesive energy density, and the "simplicity" of the model.