RSeeds: Rigid Seeding Method for Studying Heterogeneous Crystal Nucleation

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.

Differences in solvation thermodynamics of oxygenates at Pt/Al2O3 perimeter versus Pt(111) terrace sites

A prominent role of water in aqueous-phase heterogeneous catalysis is to modify free energies; however, intuition about how is based largely on pure metal surfaces or even homogeneous solutions. Using multiscale modeling with explicit liquid water molecules, we show that the influence of water on the free energies of adsorbates at metal/support interfaces is different than that on pure metal surfaces. We specifically compute free energies of solvation for methanol and its constituents on a Pt/Al₂O₃ catalyst and compare the results to analogous values calculated on a pure Pt catalyst. We find that the more hydrophilic Pt/Al₂O₃ interface leads to smaller (more positive) free energies of solvation due to an increased entropy penalty resulting from the additional work necessary to disrupt the interfacial water structure and accommodate the interfacial species. The results will be of interest in other fields, including adsorption and proteins.

Practical guide to replica exchange transition interface sampling and forward flux sampling

Path sampling approaches have become invaluable tools to explore the mechanisms and dynamics of the so-called rare events that are characterized by transitions between metastable states separated by sizable free energy barriers. Their practical application, in particular to ever more complex molecular systems, is, however, not entirely trivial. Focusing on replica exchange transition interface sampling (RETIS) and forward flux sampling (FFS), we discuss a range of analysis tools that can be used to assess the quality and convergence of such simulations, which is crucial to obtain reliable results. The basic ideas of a step-wise evaluation are exemplified for the study of nucleation in several systems with different complexities, providing a general guide for the critical assessment of RETIS and FFS simulations.

Toward enzyme-responsive polymersome drug delivery

In drug delivery, enzyme-responsive drug carriers are becoming increasingly relevant because of the growing association of disease pathology with enzyme overexpression. Polymersomes are of interest to such applications because of their tunable properties. While polymersomes open up a wide range of chemical and physical properties to explore, they also present a challenge in developing generalized rules for the synthesis of novel systems. Motivated by this issue, in this perspective, we summarize the existing knowledge on enzyme-responsive polymersomes and outline the main design choices. Then, we propose heuristics to guide the design of novel systems. Finally, we discuss the potential of an integrated approach using computer simulations and experimental studies to streamline this design process and close the existing knowledge gaps.

The Middle Science: Traversing Scale In Complex Many-Body Systems

A roadmap is developed that integrates simulation methodology and data science methods to target new theories that traverse the multiple length- and time-scale features of many-body phenomena.

Advancing Rational Control of Peptide-Surface Complexes

Understanding peptide-surface interactions is crucial for programming self-assembly of peptides at surfaces and in realizing their applications, such as biosensors and biomimetic materials. In this study, we developed insights into the dependence of a residue's interaction with a surface on its neighboring residue in a tripeptide using molecular dynamics simulations. This knowledge is integral for designing rational mutations to control peptide-surface complexes. Using graphene as our model surface, we estimated the free energy of adsorption (ΔA_ads) and extracted predominant conformations of 26 tripeptides with the motif LNR-CR-Gly, where LNR and CR are variable left-neighboring and central residues, respectively. We considered a combination of strongly adsorbing (Phe, Trp, and Arg) and weakly adsorbing (Ala, Val, Leu, Ser, and Thr) amino acids on graphene identified in a prior study to form the tripeptides. Our results indicate that ΔA_ads of a tripeptide cannot be estimated as the sum of ΔA_ads of each residue indicating that the residues in a tripeptide do not behave as independent entities. We observed that the contributions from the strongly adsorbing amino acids were dominant, which suggests that such residues could be used for strengthening peptide-graphene interactions irrespective of their neighboring residues. In contrast, the adsorption of weakly adsorbing central residues is dependent on their neighboring residues. Our structural analysis revealed that the dihedral angles of LNR are more correlated with that of CR in the adsorbed state than in bulk state. Together with ΔA_ads trends, this implies that different backbone structures of a given CR can be accessed for a similar ΔA_ads by varying the LNR. Therefore, incorporation of context effects in designing mutations can lead to desired peptide structure at surfaces. Our results also emphasize that these cooperative effects in ΔA_ads and structure are not easily predicted a priori. The collective results have applications in guiding rational mutagenesis techniques to control orientation of peptides at surfaces and in developing peptide structure prediction algorithms in adsorbed state from its sequence.

Multivalent Surface Cations Enhance Heterogeneous Freezing of Water on Muscovite Mica

Heterogeneous ice nucleation is a crucial phenomenon in various fields of fundamental and applied science. We investigate the effect of surface cations on freezing of water on muscovite mica. Mica is unique in that the exposed ion on its surface can be readily and easily exchanged without affecting other properties such as surface roughness. We investigate freezing on natural (K⁺) mica and mica in which we have exchanged K⁺ for Al³⁺, Mg²⁺, Ca²⁺, and Sr²⁺. We find that liquid water freezes at higher temperatures when ions of higher valency are present on the surface, thus exposing more of the underlying silica layer. Our data also show that the size of the ion affects the characteristic freezing temperature. Using molecular dynamics simulations, we investigate the effects that the ion valency and exposed silica layer have on the behavior of water on the surface. The results indicate that multivalent cations enhance the probability of forming large clusters of hydrogen bonded water molecules that are anchored by the hydration shells of the cations. These clusters also have a large fraction of free water that can reorient to take ice-like configurations, which are promoted by the regions on mica devoid of the ions. Thus, these clusters could serve as seedbeds for ice nuclei. The combined experimental and simulation studies shed new light on the influence of surface ions on heterogeneous ice nucleation.

A generalized deep learning approach for local structure identification in molecular simulations

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.

Free Energies of Catalytic Species Adsorbed to Pt(111) Surfaces under Liquid Solvent Calculated Using Classical and Quantum Approaches

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*, NH₂*, CO*, COH*, CH₂OH*, and C₃H₇O₃* on Pt(111) surfaces under aqueous phase and under a mixed H₂O/CH₃OH 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 H₂O 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.

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

A significant number of heterogeneously-catalyzed chemical processes occur under liquid conditions, but simulating catalyst function under such conditions is challenging when it is necessary to include the solvent molecules. The bond breaking and forming processes modeled in these systems necessitate the use of quantum chemical methods. Since molecules in the liquid phase are under constant thermal motion, simulations must also include configurational sampling. This means that multiple configurations of liquid molecules must be simulated for each catalytic species of interest. The goal of the protocol presented here is to generate and sample trajectories of configurations of liquid water molecules around catalytic species on flat transition metal surfaces in a way that balances chemical accuracy with computational expense. Specifically, force field molecular dynamics (FFMD) simulations are used to generate configurations of liquid molecules that can subsequently be used in quantum mechanics-based methods such as density functional theory or ab initio molecular dynamics. To illustrate this, in this manuscript, the protocol is used for catalytic intermediates that could be involved in the pathway for the decomposition of glycerol (C3H8O3). The structures that are generated using FFMD are modeled in DFT in order to estimate the enthalpies of solvation of the catalytic species and to identify how H2O molecules participate in catalytic decompositions.

Adsorption of amino acids on graphene: assessment of current force fields

We compare the free energies of adsorption (ΔAads) and the structural preferences of amino acids on graphene obtained using the non-polarizable force fields-Amberff99SB-ILDN/TIP3P, CHARMM36/modified-TIP3P, OPLS-AA/M/TIP3P, and Amber03w/TIP4P/2005. The amino acid-graphene interactions are favorable irrespective of the force field. While the magnitudes of ΔAads differ between the force fields, the relative free energy of adsorption across amino acids is similar for the studied force fields. ΔAads positively correlates with amino acid-graphene and negatively correlates with graphene-water interaction energies. Using a combination of principal component analysis and density-based clustering technique, we grouped the structures observed in the graphene adsorbed state. The resulting population of clusters, and the conformation in each cluster indicate that the structures of the amino acid in the graphene adsorbed state vary across force fields. The differences in the conformations of amino acids are more severe in the graphene adsorbed state compared to the bulk state for all the force fields. Our findings suggest that the force fields studied will give qualitatively consistent relative strength of adsorption across proteins but different structural preferences in the graphene adsorbed state.

Contour forward flux sampling: Sampling rare events along multiple collective variables

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.

Heterogeneous Ice Nucleation: Interplay of Surface Properties and Their Impact on Water Orientations

Ice is ubiquitous in nature, and heterogeneous ice nucleation is the most common pathway of ice formation. How surface properties affect the propensity to observe ice nucleation on that surface remains an open question. We present results of molecular dynamics studies of heterogeneous ice nucleation on model surfaces. The models surfaces considered emulate the chemistry of kaolinite, an abundant component of mineral dust. We investigate the interplay of surface lattice and hydrogen bonding properties in affecting ice nucleation. We find that lattice matching and hydrogen bonding are necessary but not sufficient conditions for observing ice nucleation at these surfaces. We correlate this behavior to the orientations sampled by the metastable supercooled water in contact with the surfaces. We find that ice is observed in cases where water molecules not only sample orientations favorable for bilayer formation but also do not sample unfavorable orientations. This distribution depends on both surface-water and water-water interactions and can change with subtle modifications to the surface properties. Our results provide insights into the diverse behavior of ice nucleation observed at different surfaces and highlight the complexity in elucidating heterogeneous ice nucleation.

Influence of carbon nanomaterial defects on the formation of protein corona

In any physiological media, carbon nanomaterials (CNM) strongly interact with biomolecules leading to the formation of biocorona, which subsequently dictate the physiological response and the fate of CNMs. Defects in CNMs play an important role not only in material properties but also in the determination of how materials interact at the nano-bio interface. In this article, we probed the influence of defect-induced hydrophilicity on the biocorona formation using micro-Raman, photoluminescence, infrared spectroscopy, electrochemistry, and molecular dynamics simulations. Our results show that the interaction of proteins (albumin and fibrinogen) with CNMs is strongly influenced by charge-transfer between them, inducing protein unfolding which enhances conformational entropy and higher protein adsorption.

PAMAM dendrimers and graphene: materials for removing aromatic contaminants from water

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.

Association of small aromatic molecules with PAMAM dendrimers

Dendrimer pockets enable association by reducing naphthalene hydration even near the dendrimer periphery.

Suppression of sub-surface freezing in free-standing thin films of a coarse-grained model of 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.

Homogeneous Nucleation of Methane Hydrate in Microsecond Molecular Dynamics Simulations

We report atomistically detailed molecular dynamics simulations of homogeneous nucleation of methane hydrate in bulk aqueous phase in the absence of any interface. Subcritical clusters of water and methane molecules are formed in the initial segment of the simulations, which then aggregate to give the critical hydrate nucleus. This occurs over time scales of several hundred nanoseconds, indicating that the formation and aggregation of subcritical clusters can contribute significantly to the overall rate of hydrate nucleation. The clusters have elements of sI hydrate structure, such as 5(12) and 5(12)6(2) cages as well as other uncommon 5(12)6(3) and 5(12)6(4) cages, but do not possess long-range order. Clusters are dynamic in nature and undergo continuous structural rearrangements.

Studying pressure denaturation of a protein by molecular dynamics simulations

Many globular proteins unfold when subjected to several kilobars of hydrostatic pressure. This "unfolding-up-on-squeezing" is counter-intuitive in that one expects mechanical compression of proteins with increasing pressure. Molecular simulations have the potential to provide fundamental understanding of pressure effects on proteins. However, the slow kinetics of unfolding, especially at high pressures, eliminates the possibility of its direct observation by molecular dynamics (MD) simulations. Motivated by experimental results-that pressure denatured states are water-swollen, and theoretical results-that water transfer into hydrophobic contacts becomes favorable with increasing pressure, we employ a water insertion method to generate unfolded states of the protein Staphylococcal Nuclease (Snase). Structural characteristics of these unfolded states-their water-swollen nature, retention of secondary structure, and overall compactness-mimic those observed in experiments. Using conformations of folded and unfolded states, we calculate their partial molar volumes in MD simulations and estimate the pressure-dependent free energy of unfolding. The volume of unfolding of Snase is negative (approximately -60 mL/mol at 1 bar) and is relatively insensitive to pressure, leading to its unfolding in the pressure range of 1500-2000 bars. Interestingly, once the protein is sufficiently water swollen, the partial molar volume of the protein appears to be insensitive to further conformational expansion or unfolding. Specifically, water-swollen structures with relatively low radii of gyration have partial molar volume that are similar to that of significantly more unfolded states. We find that the compressibility change on unfolding is negligible, consistent with experiments. We also analyze hydration shell fluctuations to comment on the hydration contributions to protein compressibility. Our study demonstrates the utility of molecular simulations in estimating volumetric properties and pressure stability of proteins, and can be potentially extended for applications to protein complexes and assemblies.

Quantifying water density fluctuations and compressibility of hydration shells of hydrophobic solutes and proteins

We probe the effects of solute length scale, attractions, and hydrostatic pressure on hydrophobic hydration shells using extensive molecular simulations. The hydration shell compressibility and water fluctuations both display a nonmonotonic dependence on solute size, with a minimum near molecular solutes and enhanced fluctuations for larger ones. These results and calculations on proteins suggest that the hydration shells of unfolded proteins are more compressible than of folded ones contributing to pressure denaturation. More importantly, the nonmonotonicity implies a solute curvature-dependent pressure sensitivity for interactions between hydrophobic solutes.