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Ionic liquids (ILs) have shown promise for applications that leverage differential gas solubility in an IL solvent, e.g., gas separations. Although most available literature provides Henry's law constants, the ability to efficiently estimate full isotherms is important for engineering design calculations. Molecular simulation can be used as a tool to predict full isotherms of gas in ILs. However, particle insertions or deletions in a charge-dense IL medium and the sluggish conformational dynamics of ILs present two sampling challenges for these systems. We therefore devised a method that uses Hamiltonian replica exchange (HREX) molecular dynamics (MD) combined with alchemical free energy calculations to compute full solubility isotherms of two different hydrofluorocarbons (HFCs) in imidazolium-based IL binary mixtures. This workflow is significantly faster than the Gibbs ensemble Monte Carlo (GEMC) simulations which fail to deal with the slow conformational relaxation caused by the sluggish dynamics of ILs. Multiple free energy estimators, including thermodynamic integration, free energy perturbation, and multistate Bennett acceptance ratio method, provided consistent results. Overall, the simulated Henry's law constant, isotherm curvature, and solubility trends match experimental results reasonably well. We close by calculating the full solubility isotherms of two HFCs in IL mixtures that have not been reported in the literature, demonstrating the potential of this method to be used for solubility prediction and setting the stage for future computational screening studies that search for the "best" IL to separate azeotropic HFC mixtures.

Heterogeneous nucleation is the dominant form of liquid-to-solid transition in nature. Although molecular simulations are most uniquely suited to studying nucleation, the waiting time to observe even a single nucleation event can easily exceed the current computational capabilities. Therefore, there exists an imminent need for methods that enable computationally fast and feasible studies of heterogeneous nucleation. Seeding is a technique that has proven to be successful at dramatically expanding the range of computationally accessible nucleation rates in simulation studies of homogeneous crystal nucleation. In this article, we introduce a new seeding method for heterogeneous nucleation called Rigid Seeding (RSeeds). Crystalline seeds are treated as pseudorigid bodies and simulated on a surface with metastable liquid above its melting temperature. This allows the seeds to adapt to the surface and identify favorable seed-surface configurations, which is necessary for reliable predictions of crystal polymorphs that form and the corresponding heterogeneous nucleation rates. We demonstrate and validate RSeeds for heterogeneous ice nucleation on a flexible self-assembled monolayer surface, a mineral surface based on kaolinite, and two model surfaces. RSeeds predicts the correct ice polymorph, exposed crystal plane, and rotation on the surface. RSeeds is semiquantitative and can be used to estimate the critical nucleus size and nucleation rate when combined with classical nucleation theory. We demonstrate that RSeeds can be used to evaluate nucleation rates spanning many orders of magnitude.

We describe and validate a free-energy-based method for computing the liquidus for binary solid-liquid phase diagrams in molecular simulations of monatomic salts. The method is demonstrated by calculating the liquidus for LiCl-KCl and MgCl 2 -KCl salt mixtures with the polarizable ion model (PIM). The free-energy-based method is cross-validated with direct coexistence simulations. Both techniques show excellent agreement with one another. Though the predictions of the PIM disagree with experiments, we use our free-energy-based approach to decouple the contributions of liquid mixture nonidealities and pure component solid-liquid equilibrium to the phase diagram. In both mixtures, the PIM accurately reproduces the liquid phase nonidealities but fails to predict the liquidus because it does not accurately predict the pure component melting temperature of LiCl or MgCl 2 .

Accurate force fields are necessary for predictive molecular simulations. However, developing force fields that accurately reproduce experimental properties is challenging. Here, we present a machine learning directed, multiobjective optimization workflow for force field parametrization that evaluates millions of prospective force field parameter sets while requiring only a small fraction of them to be tested with molecular simulations. We demonstrate the generality of the approach and identify multiple low-error parameter sets for two distinct test cases: simulations of hydrofluorocarbon (HFC) vapor-liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal phase. We discuss the challenges and implications of our force field optimization workflow.

We introduce a new Python interface for the Cassandra Monte Carlo software, molecular simulation design framework (MoSDeF) Cassandra. MoSDeF Cassandra provides a simplified user interface, offers broader interoperability with other molecular simulation codes, enables the construction of programmatic and reproducible molecular simulation workflows, and builds the infrastructure necessary for high-throughput Monte Carlo studies. Many of the capabilities of MoSDeF Cassandra are enabled via tight integration with MoSDeF. We discuss the motivation and design of MoSDeF Cassandra and proceed to demonstrate both simple use-cases and more complex workflows, including adsorption in porous media and a combined molecular dynamics - Monte Carlo workflow for computing lateral diffusivity in graphene slit pores. The examples presented herein demonstrate how even relatively complex simulation workflows can be reduced to, at most, a few files of Python code that can be version-controlled and shared with other researchers. We believe this paradigm will enable more rapid research advances and represents the future of molecular simulations., (© 2021 Wiley Periodicals LLC.)

Results from extensive molecular dynamics simulations of molten LiCl, NaCl, KCl, and RbCl over a wide range of temperatures are reported. Comparison is made between the "Polarizable Ion Model" (PIM) and the non-polarizable "Rigid Ion Model" (RIM). Densities, self-diffusivities, shear viscosities, ionic conductivities, and thermal conductivities are computed and compared with experimental data. In addition, radial distribution functions are computed from ab initio molecular dynamics simulations and compared with the two sets of classical simulations as well as experimental data. The two classical models perform reasonably well at capturing structural and dynamic properties of the four molten alkali chlorides, both qualitatively and often quantitatively. With the singular exception of liquid density, for which the PIM is more accurate than the RIM, there are few clear trends to suggest that one model is more accurate than the other for the four alkali halide systems studied here.

Accurate molecular models of pure alkali halides are a prerequisite for developing transferable models of molten salts that can predict the properties of complex salt mixtures, such as those including dissolved actinide species and metal ions. Predicting the melting point of a substance represents a rigorous test of model quality. To this end, we compute the melting points of the alkali chlorides for a popular non-polarizable and polarizable model. Neither model yields more accurate predictions of the melting points across the entire family of alkali chlorides. Further calculations suggest that this may be because neither model simultaneously represents both the solid and liquid phases with sufficient accuracy across all four alkali chlorides. We find that the deviation from experiment in the model enthalpy of melting may be a good indicator of the deviation from experiment in the model melting temperature. Since the enthalpy of melting is easier to calculate in simulation than melting temperature, it may be a useful quantity to target when developing new force fields for molten salts.

Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop ( e.g. , ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor ( e.g. , crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, i.e. , atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility of our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations., (This journal is © The Royal Society of Chemistry 2019.)

Solvent plays an important role in liquid phase heterogeneous catalysis; however, methods for calculating the free energies of catalytic phenomena at the solid-liquid interface are not well-established. For example, solvent molecules alter the energies of catalytic species and participate in catalytic reactions and can thus significantly influence catalytic performance. In this work, we begin to establish methods for calculating the free energies of such phenomena, specifically, by employing an explicit solvation method using a multiscale sampling (MSS) approach. This MSS approach combines classical molecular dynamics with density functional theory. We use it to calculate the free energies of solvation of catalytic species, specifically adsorbed NH*, NH 2 *, CO*, COH*, CH 2 OH*, and C 3 H 7 O 3 * on Pt(111) surfaces under aqueous phase and under a mixed H 2 O/CH 3 OH solvent. We compare our calculated values with analogous values from implicit solvation for validation and to identify situations where implicit solvation is sufficient versus where explicit solvent is needed to compute adsorbate free energies. Our results indicate that explicit quantum-based methods are needed when adsorbates form chemical bonds and/or strong hydrogen bonds with H 2 O solvent. Using MSS, we further separate the calculated free energies into energetic and entropic contributions in order to understand how each influences the free energy. We find that adsorbates that exhibit strong energies also exhibit strong and negative entropies, and we attribute this relationship to hydrogen bonding between the adsorbates and the solvent molecules, which provides a large energetic contribution but reduces the overall mobility of the solvent.

Many rare event transitions involve multiple collective variables (CVs), and the most appropriate combination of CVs is generally unknown a priori . We thus introduce a new method, contour forward flux sampling (cFFS), to study rare events with multiple CVs simultaneously. cFFS places nonlinear interfaces on-the-fly from the collective progress of the simulations, without any prior knowledge of the energy landscape or appropriate combination of CVs. We demonstrate cFFS on analytical potential energy surfaces and a conformational change in alanine dipeptide.

The mechanism of nucleation of clathrate hydrates of a water-soluble guest molecule is rigorously investigated with molecular dynamics (MD) simulations. Results from forward flux sampling, committor probability analysis, and twenty straightforward MD trajectories were combined to create a comprehensive understanding of the nucleation mechanism. Seven different classes of order parameters with a total of 33 individual variants were studied. We rank and evaluate the efficacy of prospective reaction coordinate models built from these order parameters and linear combinations thereof. Order parameters based upon water structuring provide a better approximation of the reaction coordinate than those based upon guest structuring. Our calculations suggest that the transition state is characterized by 2-3 partial, face-sharing 5 12 cages that form a structural motif observed in the structure II crystal. Further simulations show that once formed, this structure significantly affects the ordering of vicinal guest molecules, likely leading to hydrate nucleation. Our results contribute to the current understanding of the water-guest interplay involved in hydrate nucleation and have relevance to hydrate-based technologies that use water-soluble guest molecules (e.g., tetrahydrofuran) in mixed hydrate systems.

Many proposed applications using dendrimers, such as drug delivery and environmental remediation, involve dendrimer interactions with small molecules. Understanding the details of these interactions is important for designing dendrimers with tunable association with guest molecules. In this work, we investigate dendrimer interactions with small aromatic hydrocarbons using all-atom molecular dynamics simulations. We study the association of naphthalene (NPH)-the smallest polycyclic aromatic hydrocarbon-with 3rd-6th generation (G3-G6) polyamidoamine (PAMAM) dendrimers. Our work emphasizes that the association of small aromatic molecules with PAMAM dendrimers involves the formation of dynamic pocket-like association sites through interactions between flexible dendrimer branches and NPH molecules. The association sites are primarily formed by branches from the two outermost dendrimer subgenerations, and often involve the tertiary amine groups. Irrespective of their location on the dendrimer-whether buried or near the outer surface-these pocket-like structures lower the hydration of the associated NPH molecules. We show that on average NPH molecules with a lower hydration have a greater tendency to remain associated with the dendrimer for longer times. In general, the association sites are similar for the G3-G6 PAMAM dendrimers, indicating similarities in the association mechanisms across different dendrimer generations.

We present results from experiments and atomistic molecular dynamics simulations on the remediation of naphthalene by polyamidoamine (PAMAM) dendrimers and graphene oxide (GrO). Specifically, we investigate 3rd-6th generation (G3-G6) PAMAM dendrimers and GrO with different levels of oxidation. The work is motivated by the potential applications of these emerging nanomaterials in removing polycyclic aromatic hydrocarbon contaminants from water. Our experimental results indicate that GrO outperforms dendrimers in removing naphthalene from water. Molecular dynamics simulations suggest that the prominent factors driving naphthalene association to these seemingly disparate materials are similar. Interestingly, we find that cooperative interactions between the naphthalene molecules play a significant role in enhancing their association to the dendrimers and GrO. Our findings highlight that while selection of appropriate materials is important, the interactions between the contaminants themselves can also be important in governing the effectiveness of a given material. The combined use of experiments and molecular dynamics simulations allows us to comment on the possible factors resulting in better performance of GrO in removing polyaromatic contaminants from water.

Freezing in the vicinity of water-vapor interfaces is of considerable interest to a wide range of disciplines, most notably the atmospheric sciences. In this work, we use molecular dynamics and two advanced sampling techniques, forward flux sampling and umbrella sampling, to study homogeneous nucleation of ice in free-standing thin films of supercooled water. We use a coarse-grained monoatomic model of water, known as mW, and we find that in this model a vapor-liquid interface suppresses crystallization in its vicinity. This suppression occurs in the vicinity of flat interfaces where no net Laplace pressure in induced. Our free energy calculations reveal that the pre-critical crystalline nuclei that emerge near the interface are thermodynamically less stable than those that emerge in the bulk. We investigate the origin of this instability by computing the average asphericity of nuclei that form in different regions of the film, and observe that average asphericity increases closer to the interface, which is consistent with an increase in the free energy due to increased surface-to-volume ratios.