Long-range Electrostatic Interactions Research Articles

Influence of ion and hydration atmospheres on RNA structure and dynamics: insights from advanced theoretical and computational methods.

RNA, a highly charged biopolymer composed of negatively charged phosphate groups, defies electrostatic repulsion to adopt well-defined, compact structures. Hence, the presence of positively charged metal ions is crucial not only for RNA's charge neutralization, but they also coherently decorate the ion atmosphere of RNA to stabilize its compact fold. This feature article elucidates various modes of close RNA-ion interactions, with a special emphasis on Mg2+ as an outer-sphere and inner-sphere ion. Through examples, we highlight how inner-sphere chelated Mg2+ stabilizes RNA pseudoknots, while outer-sphere ions can also exert long-range electrostatic interactions, inducing groove narrowing, coaxial helical stacking, and RNA ring formation. In addition to investigating the RNA's ion environment, we note that the RNA's hydration environment is relatively underexplored. Our study delves into its profound interplay with the structural dynamics of RNA, employing state-of-the-art atomistic simulation techniques. Through examples, we illustrate how specific ions and water molecules are associated with RNA functions, leveraging atomistic simulations to identify preferential ion binding and hydration sites. However, understanding their impact(s) on the RNA structure remains challenging due to the involvement of large length and long time scales associated with RNA's dynamic nature. Nevertheless, our contributions and recent advances in coarse-grained simulation techniques offer insights into large-scale structural changes dynamically linked to the RNA ion atmosphere. In this connection, we also review how different cutting-edge computational simulation methods provide a microscopic lens into the influence of ions and hydration on RNA structure and dynamics, elucidating distinct ion atmospheric components and specific hydration layers and their individual and collective impacts.

Multiscale Frozen Density Embedding/Molecular Mechanics Approach for Simulating Magnetic Response Properties of Solvated Systems.

We present a three-layer hybrid quantum mechanical/quantum embedding/molecular mechanics approach for calculating nuclear magnetic resonance (NMR) shieldings and J-couplings of molecular systems in solution. The model is based on the frozen density embedding (FDE) and polarizable fluctuating charges (FQ) and fluctuating dipoles (FQFμ) force fields and permits the accurate ab initio description of short-range nonelectrostatic interactions by means of the FDE shell and cost-effective treatment of long-range electrostatic interactions through the polarizable force field FQ(Fμ). Our approach's accuracy and potential are demonstrated by studying NMR spectra of Brooker's merocyanine in aqueous and nonaqueous solutions.

Unveiling Enhanced Electrostatic Repulsion in Silica Nanosphere Assembly: Formation Dynamics of Body-Centered-Cubic Colloidal Crystals.

We demonstrate the effective establishment of long-range electrostatic interactions among colloidal silica nanospheres through acid treatment, enabling their assembly into colloidal crystals at remarkably low concentrations. This novel method overcomes the conventional limitation in colloidal silica assembly by removing entrapped NH4+ ions and enhancing the electrical double layer (EDL) thickness, offering a time-efficient alternative to increase electrostatic interactions compared with methods like dialysis. The increased EDL thickness facilitates the assembly of SiO2 nanospheres into a body-centered-cubic lattice structure at low particle concentrations, allowing for broad spectrum tunability and high tolerance to particle size polydispersity. Further, we uncover a disorder-order transition during colloidal crystallization at low particle concentrations, with the optimal concentration for crystal formation governed by both thermodynamic and kinetic factors. This work not only provides insights into assembly mechanisms but also paves the way for the design and functionalization of colloidal silica-based photonic crystals in diverse applications.

Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics.

The chemical equilibrium between self-ionized and molecular water dictates the acid-base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)-based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid-base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H2O) cells. The reversible work to separate the hydroxide and hydronium to a distance [Formula: see text] is found to converge for simulation cells containing more than 500 H2O, and a distance of [Formula: see text] 8 Å is the threshold beyond which the work to further separate the two ions becomes approximately zero. The slow convergence of the potential of mean force with system size is related to a restructuring of water and an increase of the local order around the water ions. Calculation of the dissociation equilibrium constant illustrates the key role of long-range electrostatics and entropic effects in the water autoionization process.

Manipulation of Hierarchical Chiral Self-assembly and Anion Recognition by Supramolecular Systems of β-Glucopyranoside, Pillar[5]arenes, and Polyoxometalates.

Hierarchical chiral structures have broad applications in optical devices, asymmetric catalysis, and biological systems. The delicate balance of various interactions are key to the self-assembly of chiral structures. Herein, a ternary co-assembly consisting of cationic pillar[5]arenes (P5As), anionic β-glucopyranoside (βGlcD/βGlcL), and Anderson-type polyoxometalates (POMs) were constructed. Through adjusting the stoichiometry of βGlcD, the assemblies were effectively controlled to form hierarchical nano-leaf assemblies with twisted nanoribbons in a homochiral direction. The co-assemblies exhibit strong Cotton effects, and successfully induced the chirality of Anderson-type POMs. More interestingly, by changing the central metal in Anderson-type POMs (XMo6 O24 3- (X=Cr, Al, and Ga)), even though the three clusters have the same numbers of charge and size, the hierarchical chirality of the related assemblies varied in the morphology of the assemblies and the Cotton effect in the CD spectra. Results in theoretical calculations and ITC titration indicates that the tiny difference in long-range electrostatic interaction would result in the anion recognition of POMs, modulated by βGlcD through host-guest inclusion and hydrogen bonding in the assembly process.

Visualizing interfacial collective reaction behaviour of Li-S batteries.

Benefiting from high energy density (2,600 Wh kg-1) and low cost, lithium-sulfur (Li-S) batteries are considered promising candidates for advanced energy-storage systems1-4. Despite tremendous efforts in suppressing the long-standing shuttle effect of lithium polysulfides5-7, understanding of the interfacial reactions of lithium polysulfides at the nanoscale remains elusive. This is mainly because of the limitations of in situ characterization tools in tracing theliquid-solid conversion of unstable lithium polysulfides at high temporal-spatial resolution8-10. There is an urgent need to understand the coupled phenomena inside Li-S batteries, specifically, the dynamic distribution, aggregation, deposition and dissolution of lithium polysulfides. Here, by using in situ liquid-cell electrochemical transmission electron microscopy, we directly visualized the transformation of lithium polysulfides over electrode surfaces at the atomic scale. Notably, an unexpected gathering-induced collective charge transfer of lithium polysulfides was captured onthe nanocluster active-centre-immobilized surface. It further induced an instantaneous deposition of nonequilibrium Li2S nanocrystals from the dense liquid phase of lithium polysulfides. Without mediation of active centres, the reactions followed a classical single-molecule pathway, lithium polysulfides transforming into Li2S2 and Li2S step by step. Molecular dynamics simulations indicated that the long-range electrostatic interaction between active centres and lithium polysulfides promoted the formation of adense phase consisting of Li+ and Sn2- (2 < n ≤ 6), and the collective charge transfer in thedense phase was further verified by ab initio molecular dynamics simulations. The collective interfacial reaction pathway unveils a new transformation mechanism and deepens the fundamental understanding of Li-S batteries.

Charge-driven liquid-crystalline behavior of ligand-functionalized nanorods in apolar solvent.

Concentrated colloidal suspensions of nanorods often exhibit liquid-crystalline (LC) behavior. The transition to a nematic LC phase, with long-range orientational order of the particles, is usually well-captured by Onsager's theory for hard rods, at least qualitatively. The theory shows how the volume fraction at the transition decreases with increasing aspect ratio of the rods. It also explains that the long-range electrostatic repulsive interaction occurring between rods stabilized by their surface charge can significantly increase their effective diameter, resulting in a decrease in the volume fraction at the transition, as compared to sterically stabilized rods. Here, we report on a system of ligand-stabilized LaPO4 nanorods, of aspect ratio ≈11, dispersed in apolar medium exhibiting the counter-intuitive observation that the onset of nematic self-assembly occurs at an extremely low volume fraction of ≈0.25%, which is lower than observed (≈3%) with the same particles when charged-stabilized in polar solvent. Furthermore, the nanorod volume fraction at the transition increases with increasing concentration of ligands, in a similar way as in polar media where increasing the ionic strength leads to surface charge screening. This peculiar system was investigated by dynamic light scattering, Fourier-transform infrared spectroscopy, zetametry, electron microscopy, polarized light microscopy, photoluminescence measurements, and X-ray scattering. Based on these experimental data, we formulate several tentative scenarios that might explain this unexpected phase behavior. However, at this stage, its full understanding remains a pending theoretical challenge. Nevertheless, this study shows that dispersing anisotropic nanoparticles in an apolar solvent may sometimes lead to spontaneous ordering events that defy our intuitive ideas about colloidal systems.

PANNA 2.0: Efficient neural network interatomic potentials and new architectures.

We present the latest release of PANNA 2.0 (Properties from Artificial Neural Network Architectures), a code for the generation of neural network interatomic potentials based on local atomic descriptors and multilayer perceptrons. Built on a new back end, this new release of PANNA features improved tools for customizing and monitoring network training, better graphics processing unit support including a fast descriptor calculator, new plugins for external codes, and a new architecture for the inclusion of long-range electrostatic interactions through a variational charge equilibration scheme. We present an overview of the main features of the new code, and several benchmarks comparing the accuracy of PANNA models to the state of the art, on commonly used benchmarks as well as richer datasets.

Nucleoside Analog Reverse-Transcriptase Inhibitors in Membrane Environment: Molecular Dynamics Simulations.

The behavior of four drugs from the family of nucleoside analog reverse-transcriptase inhibitors (zalcitabine, stavudine, didanosine, and apricitabine) in a membrane environment was traced using molecular dynamics simulations. The simulation models included bilayers and monolayers composed of POPC and POPG phospholipids. It was demonstrated that the drugs have a higher affinity towards POPG membranes than POPC membranes due to attractive long-range electrostatic interactions. The results obtained for monolayers were consistent with those obtained for bilayers. The drugs accumulated in the phospholipid polar headgroup region. Two adsorption modes were distinguished. They differed in the degree of penetration of the hydrophilic headgroup region. Hydrogen bonds between drug molecules and phospholipid heads were responsible for adsorption. It was shown that apricitabine penetrated the hydrophilic part of the POPC and POPG membranes more effectively than the other drugs. Van der Waals interactions between S atoms and lipids were responsible for this.

EE-ONIOM-CT Method to Efficiently Account for the Missing Interactions in ONIOM: Energies and Analytic Gradients.

Hybrid methods such as ONIOM (QM:QM) are widely used for the study of local processes in large systems. However, the intrinsic need for system partitioning often leads to a less-than-desirable performance for some important chemical processes. This is due to the missing interactions in the chemically important model region (i.e., active site) of the high-level theory. The missing interactions can be categorized into two classes, viz. charge transfer (i.e., charge redistribution) and long-range electrostatic interactions. Our group presented two entirely different methods to treat these deficiencies individually. ONIOM-CT and ONIOM-EE methods have been demonstrated to improve the performance of ONIOM by incorporating charge transfer and missing electrostatic interactions, respectively. In general, the inclusion of the missing interactions separately in two different calculations may not be sufficient to reach a high accuracy. Thus, it is highly desirable to develop a method to correct both deficiencies simultaneously. In this work, we aim to connect the methods ONIOM-CT and ONIOM-EE for a more comprehensive treatment. A "stepwise" model was found to be necessary for a robust performance. This model employs a stepwise procedure by first satisfying the ONIOM-CT condition for charge balance before accounting for the electrostatic interactions from the rest of the system perturbatively. This has the advantage of easy interpretation due to the clear separation of the two effects. We demonstrate the performance of our method using embedding charges determined from a Mulliken population analysis. An efficient analytic gradient expression for this method is derived and implemented by requiring three sets of z-vector self-consistent equations. The performance of our method is assessed against full system calculations in high-level theory for a set of three proton transfer reactions representing different degrees of electrostatic embedding.

Q-pac: A Python package for machine learned charge equilibration models.

Many state-of-the art machine learning (ML) interatomic potentials are based on a local or semi-local (message-passing) representation of chemical environments. They, therefore, lack a description of long-range electrostatic interactions and non-local charge transfer. In this context, there has been much interest in developing ML-based charge equilibration models, which allow the rigorous calculation of long-range electrostatic interactions and the energetic response of molecules and materials to external fields. The recently reported kQEq method achieves this by predicting local atomic electronegativities using Kernel ML. This paper describes the q-pac Python package, which implements several algorithmic and methodological advances to kQEq and provides an extendable framework for the development of ML charge equilibration models.

Discovery of a Series of Indane-Containing NBTIs with Activity against Multidrug-Resistant Gram-Negative Pathogens.

The rise of multidrug-resistant (MDR) Gram-negative bacteria is a major global health problem necessitating the discovery of new classes of antibiotics. Novel bacterial topoisomerase inhibitors (NBTIs) target the clinically validated bacterial type II topoisomerases with a distinct binding site and mechanism of action to fluoroquinolone antibiotics, thus avoiding cross-resistance to this drug class. Here we report the discovery of a series of NBTIs incorporating a novel indane DNA binding moiety. X-ray cocrystal structures of compounds 2 and 17a bound to Staphylococcus aureus DNA gyrase-DNA were determined, revealing specific interactions with the enzyme binding pocket at the GyrA dimer interface and a long-range electrostatic interaction between the basic amine in the linker and the carboxylate of Asp83. Exploration of the structure-activity relationship within the series led to the identification of lead compound 18c, which showed potent broad-spectrum activity against a panel of MDR Gram-negative bacteria.

Hierarchical Model of Weak Polyelectrolytes with Ionization and Conformation Consistency

Conventional models of weak polyelectrolytes are formulated in terms of either an average degree of ionization or a rigid polymer conformation. While the decoupling of segment-level ionization and polymer structure simplifies the theoretical procedure and is beneficial from a computational perspective, it misses intrachain correlations (i.e., interactions beyond adjacent monomers) that are important for systems at a low polymer concentration with strong electrostatic interactions. In this work, we propose a hierarchical method that incorporates the liquid-state theories with the site-binding model to account for the effects of the local ionic environment on both inter- and intrachain correlations. A wormlike-chain model is introduced to describe the coupling of polymer conformation and monomeric ionization with the long-range electrostatic interactions accounted for by using the Gibbs–Bogoliubov variational principle. Through an extensive comparison of the theoretical predictions with experimental titration curves for poly(acrylic acid) in different alkali chloride solutions, we demonstrate that the hierarchical model is able to quantify the charging behavior of weak polyelectrolytes over a broad range of solution conditions.

Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding.

In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemistry to materials science. While most current MLPs are based on environment-dependent atomic energies, the limitations of this locality approximation can be overcome, e.g., in fourth-generation MLPs, which incorporate long-range electrostatic interactions based on an equilibrated global charge distribution. Apart from the considered interactions, the quality of MLPs crucially depends on the information available about the system, i.e., the descriptors. In this work we show that including─in addition to structural information─the electrostatic potential arising from the charge distribution in the atomic environments significantly improves the quality and transferability of the potentials. Moreover, the extended descriptor allows current limitations of two- and three-body based feature vectors to be overcome regarding artificially degenerate atomic environments. The capabilities of such an electrostatically embedded fourth-generation high-dimensional neural network potential (ee4G-HDNNP), which is further augmented by pairwise interactions, are demonstrated for NaCl as a benchmark system. Employing a data set containing only neutral and negatively charged NaCl clusters, even small energy differences between different cluster geometries can be resolved, and the potential shows an impressive transferability to positively charged clusters as well as the melt.

Dielectric Saturation in Water from a Long-Range Machine Learning Model.

Machine learning-based neural network potentials have the ability to provide ab initio-level predictions while reaching large length and time scales often limited to empirical force fields. Traditionally, neural network potentials rely on a local description of atomic environments to achieve this scalability. These local descriptions result in short-range models that neglect long-range interactions necessary for processes like dielectric screening in polar liquids. Several approaches to including long-range electrostatic interactions within neural network models have appeared recently, and here we investigate the transferability of one such model, the self-consistent field neural network (SCFNN), which focuses on learning the physics associated with long-range response. By learning the essential physics, one can expect that such a neural network model should exhibit at least partial transferability. We illustrate this transferability by modeling dielectric saturation in a SCFNN model of water. We show that the SCFNN model can predict nonlinear response at high electric fields, including saturation of the dielectric constant, without training the model on these high field strengths and the resulting liquid configurations. We then use these simulations to examine the nuclear and electronic structure changes underlying dielectric saturation. Our results suggest that neural network models can exhibit transferability beyond the linear response regime and make genuine predictions when the relevant physics is properly learned.

Thermodynamics of Light-Induced Nanoscale Polar Structures in Ferroelectric Superlattices.

We study the thermodynamics of nanoscale polar structures in PbTiO3/SrTiO3 ferroelectric superlattices induced by above-bandgap optical excitation using a phase-field model explicitly considering both structural and electronic processes. We demonstrate that the light-excited carriers provide the charge compensation of polarization bound charges and the lattice thermal energy, both of which are key to the thermodynamic stabilization of a previously observed supercrystal, a three-dimensionally periodic nanostructure, within a window of substrate strains, while different mechanical and electrical boundary conditions can stabilize a number of other nanoscale polar structures by balancing the competing short-range exchange interactions responsible for the domain wall energy and long-range electrostatic and elastic interactions. The insights into the light-induced formation and richness of nanoscale structures from this work offer theoretical guidance for exploring and manipulating the thermodynamic stability of nanoscale polar structures employing a combination of thermal, mechanical, and electrical stimuli as well as light.

Machine Learning Assisted Simulations of Electrochemical Interfaces: Recent Progress and Challenges.

The electrochemical interface, where the adsorption of reactants and electrocatalytic reactions take place, has long been a focus of attention. Some of the important processes on it tend to possess relatively slow kinetic characteristics, which are usually beyond the scope of ab initio molecular dynamics. The newly emerging technique, machine learning methods, provides an alternative approach to achieve thousands of atoms and nanosecond time scale while ensuring precision and efficiency. In this Perspective, we summarize in detail the recent progress and achievements made by the introduction of machine learning to simulate electrochemical interfaces, and focus on the limitations of current machine learning models, such as accurate descriptions of long-range electrostatic interactions and the kinetics of the electrochemical reactions occurring at the interface. Finally, we further point out the future directions for machine learning to expand in the field of electrochemical interfaces.

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials that currently lacks sufficient training data is oxides, which are critical for the development of OER catalysts. To address this, we developed the OC22 dataset, consisting of 62,331 DFT relaxations (~9,854,504 single point calculations) across a range of oxide materials, coverages, and adsorbates. We define generalized total energy tasks that enable property prediction beyond adsorption energies; we test baseline performance of several graph neural networks; and we provide pre-defined dataset splits to establish clear benchmarks for future efforts. In the most general task, GemNet-OC sees a ~36% improvement in energy predictions when combining the chemically dissimilar OC20 and OC22 datasets via fine-tuning. Similarly, we achieved a ~19% improvement in total energy predictions on OC20 and a ~9% improvement in force predictions in OC22 when using joint training. We demonstrate the practical utility of a top performing model by capturing literature adsorption energies and important OER scaling relationships. We expect OC22 to provide an important benchmark for models seeking to incorporate intricate long-range electrostatic and magnetic interactions in oxide surfaces. Dataset and baseline models are open sourced, and a public leaderboard is available to encourage continued community developments on the total energy tasks and data.

SH3 binding pathway and kinetics in the presence of salt.

Protein-protein interactions are involved in a wide range of cellular processes, like cell signaling, in which these interactions often bind to intrinsically disordered proteins. SH3 domains are common protein interaction domains which are typically negatively charged and typically bind proline-rich disordered segments, which are positively charged. This project focuses on the role of electrostatics in the binding pathway of the yeast protein Abp1 SH3 domain (AbpSH3) and the intrinsically disordered peptide ArkA. We are investigating the binding pathway by simulating ArkA-AbpSH3 binding using molecular dynamics in the presence of sodium chloride, which serves to screen electrostatic interactions. Previous simulations in the absence of salt showed that ArkA initially forms a disordered encounter complex with AbpSH3 before reaching the fully bound state. In the absence of salt, the encounter complex, characterized by many transient non-specific electrostatic interactions, forms rapidly followed by a slower transition to the fully bound state. The addition of 800 mM sodium chloride leads to a slower encounter complex formation and a decrease in electrostatic contacts. Screening of long-range electrostatic interactions could also disrupt electrostatic steering to the binding surface during the initial encounter complex formation. Simulating the binding between ApbSH3 and ArkA in the presence of salt can provide insight into the role of electrostatics in SH3 binding in general and can be compared to experimental data on AbpSH3 binding at different salt concentrations.

Impact of Mineralizing Agents on Aluminum Distribution and Acidity of ZSM-5 Zeolites.

Intensive research on improving the catalytic properties of zeolites is focused on modulating their acidity and the distribution of associated Al sites. Herein, by studying a series of ZSM-5 zeolites over a broad range of Al content, we demonstrate how the nature of the mineralizing agent (F- or OH- ) used in hydrothermal syntheses directly impacts Al sites distribution. The proportions of Al sites, probed by 27 Al NMR, depend on the Si/Al ratio for F- , but remain identical for OH- (from Si/Al=30 to 760). This leads to contrasting variations in weak and strong acidities. Such opposite effect of mineralizers is explained by the spatial location of negative charges and the resulting balance between short- and long-range electrostatic interactions. This understanding paves the way for additional and simple opportunities to control zeolites' acidity.