Towards predictive control of reversible nanoparticle assembly with solid-binding proteins.

Although a broad range of ligand-functionalized nanoparticles and physico-chemical triggers have been exploited to create stimuli-responsive colloidal systems, little attention has been paid to the reversible assembly of unmodified nanoparticles with non-covalently bound proteins. Previously, we reported that a derivative of green fluorescent protein engineered with oppositely located silica-binding peptides mediates the repeated assembly and disassembly of 10-nm silica nanoparticles when pH is toggled between 7.5 and 8.5. We captured the subtle interplay between interparticle electrostatic repulsion and their protein-mediated short-range attraction with a multiscale model energetically benchmarked to collective system behavior captured by scattering experiments. Here, we show that both solution conditions (pH and ionic strength) and protein engineering (sequence and position of engineered silica-binding peptides) provide pathways for reversible control over growth and fragmentation, leading to clusters ranging in size from 25 nm protein-coated particles to micrometer-size aggregate. We further find that the higher electrolyte environment associated with successive cycles of base addition eventually eliminates reversibility. Our model accurately predicts these multiple length scales phenomena. The underpinning concepts provide design principles for the dynamic control of other protein- and particle-based nanocomposites.

The evolution of solvation symmetry and composition in Zn halide aqueous solutions from dilute to extreme concentrations.

The emergence of cation-anion species, or contact ion pairs, is fundamental to understanding the physical properties of aqueous solutions when moving from the ideal, low-concentration limit to the manifestly non-ideal limits of very high solute concentration or constituent ion activity. We focus here on Zn halide solutions both as a model system and also as an exemplar of the applications spanning from (i) electrical energy storage via the paradigm of water in salt electrolyte (WiSE) to (ii) the physical chemistry of brines in geochemistry to (iii) the long-standing problem of nucleation. Using a combination of experimental and theoretical approaches we quantify the halide coordination number and changing coordination geometry without embedded use of theoretical equilibrium constants. These results and the associated methods, notably including the use of valence-to-core X-ray emission spectroscopy, provide new insights into the Zn halide system and new research directions in the physical chemistry of concentrated electrolytes.

Computational and Experimental Determination of the Properties, Structure, and Stability of Peptoid Nanosheets and Nanotubes.

Peptoids (N-substituted glycines) are a group of highly controllable peptidomimetic polymers. Amphiphilic diblock peptoids have been engineered to assemble crystalline nanospheres, nanofibrils, nanosheets, and nanotubes with biochemical, biomedical, and bioengineering applications. The mechanical properties of peptoid nanoaggregates and their relationship to the emergent self-assembled morphologies have been relatively unexplored and are critical for the rational design of peptoid nanomaterials. In this work, we consider a family of amphiphilic diblock peptoids consisting of a prototypical tube-former (Nbrpm6Nc6, a NH2-capped hydrophobic block of six N-((4-bromophenyl)methyl)glycine residues conjugated to a polar NH3(CH2)5CO tail), a prototypical sheet-former (Nbrpe6Nc6, where the hydrophobic block comprises six N-((4-bromophenyl)ethyl)glycine residues), and an intermediate sequence that forms mixed structures ((NbrpeNbrpm)3Nc6). We combine all-atom molecular dynamics simulations and atomic force microscopy to determine the mechanical properties of the self-assembled 2D crystalline nanosheets and relate these properties to the observed self-assembled morphologies. We find good agreement between our computational predictions and experimental measurements of Young's modulus of crystalline nanosheets. A computational analysis of the bending modulus along the two axes of the planar crystalline nanosheets reveals bending to be more favorable along the axis in which the peptoids stack by interdigitation of the side chains compared to that in which they form columnar crystals with π-stacked side chains. We construct molecular models of nanotubes of the Nbrpm6Nc6 tube-forming peptoid and predict a stability optimum in good agreement with experimental measurements. A theoretical model of nanotube stability suggests that this optimum is a free energy minimum corresponding to a "Goldilocks" tube radius at which capillary wave fluctuations in the tube wall are minimized.

Birth of the Hydrated Electron via Charge-Transfer-to-Solvent Excitation of Aqueous Iodide.

A primary means to generate hydrated electrons in laboratory experiments is excitation to the charge-transfer-to-solvent (CTTS) state of a solute such as I-(aq), but this initial step in the genesis of e-(aq) has never been simulated directly using ab initio molecular dynamics. We report the first such simulations, combining ground- and excited-state simulations of I-(aq) with a detailed analysis of fluctuations in the Coulomb potential experienced by the nascent solvated electron. What emerges is a two-step picture of the evolution of e-(aq) starting from the CTTS state: I-(aq) + hν → I-*(aq) → I•(aq) + e-(aq). Notably, the equilibrated ground state of e-(aq) evolves from I-*(aq) without any nonadiabatic transitions, simply as a result of solvent reorganization. The methodology used here should be applicable to other photochemical electron transfer processes in solution, an important class of problems directly relevant to photocatalysis and energy transfer.

Discrete Orientations of Interfacial Waters Direct Crystallization of Mica-Binding Proteins.

Understanding the basis of templated molecular assembly on a solid surface requires a fundamental comprehension of both short- and long-range aqueous response to the surface under a variety of solution conditions. Herein we provide a detailed picture of how the molecular-scale response to different mica surfaces yields distinct solvent orientations that produce quasi-static directional potentials onto which macromolecules can adsorb. We connect this directionality to observed (a)symmetric epitaxial alignment of designed proteins onto these surfaces, corroborate our findings with 3D atomic force microscopy experiments, and identify slight differences in surface structure as the origin of this effect. Our work provides a detailed picture of the intrinsic electrolyte response in the vicinity of mineral interfaces, with clear predictions for experiment, and highlights the role of solvent on the predictive assembly of hierarchical materials on mineral surfaces.

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction.

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

Spiers Memorial Lecture: Assembly-based pathways of crystallization.

Solution crystallization of materials ranging from simple salts to complex supramolecular assemblies has long been viewed through the lens of classical nucleation and growth theories in which monomeric building blocks assemble into ordered structures through inherent thermal fluctuations that overcome a free energy barrier and continue to grow by the addition of such units to atomic steps. However, recent observations have revealed a rich set of hierarchical pathways during both nucleation and growth involving species of a higher order than monomers. While many studies have investigated and deduced the mechanisms underlying hierarchical nucleation pathways, much less research has been directed towards the development of a mechanistic picture of growth by the assembly of more complex units. Here, we review recent investigations into crystal growth by particle attachment, with an emphasis on oriented attachment. We discuss the relationship between interfacial structure, interparticle forces, and attachment dynamics, discuss the consequences of size dependent phase stability, and examine the impact of the ligand-functionalization of primary particles.

Predictive Theoretical Framework for Dynamic Control of Bioinspired Hybrid Nanoparticle Self-Assembly.

At-will tailoring of the formation and reconfiguration of hierarchical structures is a key goal of modern nanomaterial design. Bioinspired systems comprising biomacromolecules and inorganic nanoparticles have potential for new functional material structures. Yet, consequential challenges remain because we lack a detailed understanding of the temporal and spatial interplay between participants when it is mediated by fundamental physicochemical interactions over a wide range of scales. Motivated by a system in which silica nanoparticles are reversibly and repeatedly assembled using a homobifunctional solid-binding protein and single-unit pH changes under near-neutral solution conditions, we develop a theoretical framework where interactions at the molecular and macroscopic scales are rigorously coupled based on colloidal theory and atomistic molecular dynamics simulations. We integrate these interactions into a predictive coarse-grained model that captures the pH-dependent reversibility and accurately matches small-angle X-ray scattering experiments at collective scales. The framework lays a foundation to connect microscopic details with the macroscopic behavior of complex bioinspired material systems and to control their behavior through an understanding of both equilibrium and nonequilibrium characteristics.

Hierarchical Self-Assembly Pathways of Peptoid Helices and Sheets.

Peptoids (N-substituted glycines) are a class of tailorable synthetic peptidomic polymers. Amphiphilic diblock peptoids have been engineered to assemble 2D crystalline lattices with applications in catalysis and molecular separations. Assembly is induced in an organic solvent/water mixture by evaporating the organic phase, but the assembly pathways remain uncharacterized. We conduct all-atom molecular dynamics simulations of Nbrpe6Nc6 as a prototypical amphiphilic diblock peptoid comprising an NH2-capped block of six hydrophobic N-((4-bromophenyl)ethyl)glycine residues conjugated to a polar NH3(CH2)5CO tail. We identify a thermodynamically controlled assembly mechanism by which monomers assemble into disordered aggregates that self-order into 1D chiral helical rods then 2D achiral crystalline sheets. We support our computational predictions with experimental observations of 1D rods using small-angle X-ray scattering, circular dichroism, and atomic force microscopy and 2D crystalline sheets using X-ray diffraction and atomic force microscopy. This work establishes a new understanding of hierarchical peptoid assembly and principles for the design of peptoid-based nanomaterials.

Probing the thermodynamics and kinetics of ethylene carbonate reduction at the electrode-electrolyte interface with molecular simulations.

Understanding the formation of the solid-electrolyte interphase (SEI) in lithium-ion batteries is an ongoing area of research due to its high degree of complexity and the difficulties encountered by experimental studies. Herein, we investigate the initial stage of SEI growth, the reduction reaction of ethylene carbonate (EC), from both a thermodynamic and a kinetic approach with theory and molecular simulations. We employed both the potential distribution theorem and the Solvation Method based on Density (SMD) to EC solvation for the estimation of reduction potentials of Li+, EC, and Li+-solvating EC (s-EC) as well as reduction rate constants of EC and s-EC. We find that solvation effects greatly influence these quantities of interest, particularly the Li+/Li reference electrode potential in EC solvent. Furthermore, we also compute the inner- and outer-sphere reorganization energies for both EC and s-EC at the interface of liquid EC and a hydroxyl-terminated graphite surface, where total reorganization energies are predicted to be 76.6 and 88.9 kcal/mol, respectively. With the computed reorganization energies, we estimate reduction rate constants across a range of overpotentials and show that EC has a larger electron transfer rate constant than s-EC at equilibrium, despite s-EC being more thermodynamically favorable. Overall, this manuscript demonstrates how ion solvation effects largely govern the prediction of reduction potentials and electron transfer rate constants at the electrode-electrolyte interface.

Visualizing Solution Structure at Solid-Liquid Interfaces using Three-Dimensional Fast Force Mapping.

Amongst the challenges for a variety of research fields are the visualization of solid-liquid interfaces and understanding how they are affected by the solution conditions such as ion concentrations, pH, ligands, and trace additives, as well as the underlying crystallography and chemistry. In this context, three-dimensional fast force mapping (3D FFM) has emerged as a promising tool for investigating solution structure at interfaces. This capability is based on atomic force microscopy (AFM) and allows the direct visualization of interfacial regions in three spatial dimensions with sub-nanometer resolution. Here we provide a detailed description of the experimental protocol for acquiring 3D FFM data. The main considerations for optimizing the operating parameters depending on the sample and application are discussed. Moreover, the basic methods for data processing and analysis are discussed, including the transformation of the measured instrument observables into tip-sample force maps that can be linked to the local solution structure. Finally, we shed light on some of the outstanding questions related to 3D FFM data interpretation and how this technique can become a central tool in the repertoire of surface science.

Natural Charge-Transfer Analysis: Eliminating Spurious Charge-Transfer States in Time-Dependent Density Functional Theory via Diabatization, with Application to Projection-Based Embedding.

For many types of vertical excitation energies, linear-response time-dependent density functional theory (LR-TDDFT) offers a useful degree of accuracy combined with unrivaled computational efficiency, although charge-transfer excitation energies are often systematically and dramatically underestimated, especially for large systems and those that contain explicit solvent. As a result, low-energy electronic spectra of solution-phase chromophores often contain tens to hundreds of spurious charge-transfer states, making LR-TDDFT needlessly expensive in bulk solution. Intensity borrowing by these spurious states can affect intensities of the valence excitations, altering electronic bandshapes. At higher excitation energies, it is difficult to distinguish spurious charge-transfer states from genuine charge-transfer-to-solvent (CTTS) excitations. In this work, we introduce an automated diabatization that enables fast and effective screening of the CTTS acceptor space in bulk solution. Our procedure introduces "natural charge-transfer orbitals" that provide a means to isolate orbitals that are most likely to participate in a CTTS excitation. Projection of these orbitals onto solvent-centered virtual orbitals provides a criterion for defining the most important solvent molecules in a given excitation and be used as an automated subspace selection algorithm for projection-based embedding of a high-level description of the CTTS state in a lower-level description of its environment. We apply this method to an ab initio molecular dynamics trajectory of I-(aq) and report the lowest-energy CTTS band in the absorption spectrum. Our results are in excellent agreement with the experiment, and only one-third of the water molecules in the I-(H2O)96 simulation cell need to be described with LR-TDDFT to obtain excitation energies that are converged to <0.1 eV. The tools introduced herein will improve the accuracy, efficiency, and usability of LR-TDDFT in solution-phase environments.

Toward a First-Principles Framework for Predicting Collective Properties of Electrolytes.

Given the universal importance of electrolyte solutions, it is natural to expect that we have a nearly complete understanding of the fundamental properties of these solutions (e.g., the chemical potential) and that we can therefore explain, predict, and control the phenomena occurring in them. In fact, reality falls short of these expectations. But, recent advances in the simulation and modeling of electrolyte solutions indicate that it should soon be possible to make progress toward these goals. In this Account, we will discuss the use of first-principles interaction potentials based in quantum mechanics (QM) to enhance our understanding of electrolyte solutions. Specifically, we will focus on the use of quantum density functional theory (DFT) combined with molecular dynamics simulation (DFT-MD) as the foundation for our approach. The overarching concept is to understand and accurately reproduce the balance between local or short-ranged (SR) structural details and long-range (LR) correlations, allowing the prediction of the thermodynamics of both single ions in solution as well as the collective interactions characterized by activity/osmotic coefficients. In doing so, relevant collective motions and driving forces characterized by chemical potentials can be determined.In this Account, we will make the case that understanding electrolyte solutions requires a faithful QM representation of the SR nature of the ion-ion, ion-water, and water-water interactions. However, the number of molecules that is required for collective behavior makes the direct application of high-level QM methods that contain the best SR physics untenable, making methods that balance accuracy and efficiency a practical goal. Alternatives such as continuum solvent models (CSMs) and empirically based classical molecular dynamics have been extensively employed to resolve this problem but without yet overcoming the fundamental issue of SR accuracy. We will demonstrate that accurately describing the SR interaction is imperative for predicting both intrinsic properties, namely, at infinite dilution, and collective properties of electrolyte solutions.DFT has played an important role in our understanding of condensed phase systems, e.g., bulk liquid water, the air-water interface, ions in bulk, and at the air-water interface. This approach holds huge promise to provide benchmark calculations of electrolyte solution properties that will allow for the development and improvement of more efficient methods, as well as an enhanced understanding of fundamental phenomena. However, the standard protocol using the generalized gradient approximation with van der Waals (vdW) correction requires improvement in order to achieve a high level of quantitative accuracy. Simply simulating with higher level DFT functionals may not be the best route considering the significant computational cost. Alternative methods of incorporating information from higher levels of QM should be explored; e.g., using force matching techniques on small clusters, where high level benchmark calculations are possible, to develop ideal correction terms to the DFT functional is a promising possibility. We argue that DFT with statistical mechanics is becoming an increasingly useful framework enabling the prediction of collective electrolyte properties.

Ion-dependent protein-surface interactions from intrinsic solvent response.

The phyllosilicate mineral muscovite mica is widely used as a surface template for the patterning of macromolecules, yet a molecular understanding of its surface chemistry under varying solution conditions, required to predict and control the self-assembly of adsorbed species, is lacking. We utilize all-atom molecular dynamics simulations in conjunction with an electrostatic analysis based in local molecular field theory that affords a clean separation of long-range and short-range electrostatics. Using water polarization response as a measure of the electric fields that arise from patterned, surface-bound ions that direct the adsorption of charged macromolecules, we apply a Landau theory of forces induced by asymmetrically polarized surfaces to compute protein-surface interactions for two muscovite-binding proteins (DHR10-mica6 and C98RhuA). Comparison of the pressure between surface and protein in high-concentration KCl and NaCl aqueous solutions reveals ion-specific differences in far-field protein-surface interactions, neatly capturing the ability of ions to modulate the surface charge of muscovite that in turn selectively attracts one binding face of each protein over all others.

Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data.

The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. The deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

Correlation function approach for diffusion in confined geometries.

This paper describes a formalism for extracting spatially varying transport coefficients from simulations of a molecular fluid in a nanochannel. This approach is applied to self-diffusion of a Lennard-Jones fluid confined between two parallel surfaces. A numerical grid is laid over the domain confining the fluid, and fluid properties are projected onto the grid cells. The time correlation functions between properties in different grid cells are calculated and can be used as the basis for a fitting procedure for extracting spatially varying diffusion coefficients from the simulation. Results for the Lennard-Jones system show that transport behavior varies sharply near the liquid-solid boundary and that the changes depend on the details of the liquid-solid interaction. A quantitative difference between the reduced and detailed models is discussed. It is found that the difference could be associated with assumptions about the form of the transport equations at molecular scales in lieu of problems with the method itself. The study suggests that this approach to fitting molecular simulations to continuum equations may guide the development of appropriate coarse-grained equations to model transport phenomena at nanometer scales.

MARTINI-Compatible Coarse-Grained Model for the Mesoscale Simulation of Peptoids.

Peptoids (poly-N-substituted glycines) are a class of synthetic polymers that are regioisomers of peptides (poly-C-substituted glycines), in which the point of side-chain connectivity is shifted from the backbone C to the N atom. Peptoids have found diverse applications as peptidomimetic drugs, protein mimetic polymers, surfactants, and catalysts. Computational modeling is valuable in the understanding and design of peptoid-based nanomaterials. In this work, we report the bottom-up parameterization of coarse-grained peptoid force fields based on the MARTINI peptide force field against all-atom peptoid simulation data. Our parameterization pipeline iteratively refits coarse-grained bonded interactions using iterative Boltzmann inversion and nonbonded interactions by matching the potential of mean force for chain extension. We assure good sampling of the amide bond cis/trans isomerizations in the all-atom simulation data using parallel bias metadynamics. We develop coarse-grained models for two representative peptoids-polysarcosine (poly(N-methyl glycine)) and poly(N-((4-bromophenyl)ethyl)glycine)-and show their structural and thermodynamic properties to be in excellent accord with all-atom calculations but up to 25-fold more efficient and compatible with MARTINI force fields. This work establishes a new rigorously parameterized coarse-grained peptoid force field for the understanding and design of peptoid nanomaterials at length and time scales inaccessible to all-atom calculations.

Solvent reaction coordinate for an SN2 reaction.

We study the prototypical SN2 reaction Cl- + CH3Cl → CH3Cl + Cl- in water using quantum mechanics/molecular mechanics (QM/MM) computer simulations with transition path sampling and inertial likelihood maximization. We have identified a new solvent coordinate to complement the original atom-exchange coordinate used in the classic analysis by Chandrasekhar, Smith, and Jorgensen [J. Am. Chem. Soc. 107, 154 (1985)]. The new solvent coordinate quantifies instantaneous solvent-induced polarization relative to the equilibrium average charge density at each point along the reaction pathway. On the basis of likelihood scores and committor distributions, the new solvent coordinate improves upon the description of solvent dynamical effects relative to previously proposed solvent coordinates. However, it does not increase the transmission coefficient or the accuracy of a transition state theory rate calculation.

Method for Accurately Predicting Solvation Structure.

Accurately predicting the molecular structure of solutions is a fundamental scientific challenge. Using quantum mechanical density functional theory (DFT) to make these predictions is hindered by significant variation depending on which DFT functional is used. Here, we present a simple metric that can determine the reliability of a DFT functional for predicting solvation structure. We then show that including a simple interaction term to correct this metric leads to quantitative agreement with experimental measurements of liquid structure. We demonstrate the utility of this method by using it to accurately describe the hydration structure around the Na+ and K+ ions as well as the structural properties of pure water with a computationally cheap functional.