Long-range Electrostatic Interactions Research Articles

Density-based long-range electrostatic descriptors for machine learning force fields.

This study presents a long-range descriptor for machine learning force fields that maintains translational and rotational symmetry, similar to short-range descriptors while being able to incorporate long-range electrostatic interactions. The proposed descriptor is based on an atomic density representation and is structurally similar to classical short-range atom-centered descriptors, making it straightforward to integrate into machine learning schemes. The effectiveness of our model is demonstrated through comparative analysis with the long-distance equivariant (LODE) [Grisafi and Ceriotti, J. Chem. Phys. 151, 204105 (2019)] descriptor. In a toy model with purely electrostatic interactions, our model achieves errors below 0.1%, worse than LODE but still very good. For real materials, we perform tests for liquid NaCl, rock salt NaCl, and solid zirconia. For NaCl, the present descriptors improve on short-range density descriptors, reducing errors by a factor of two to three and coming close to message-passing networks. However, for solid zirconia, no improvements are observed with the present approach, while message-passing networks reduce the error by almost a factor of two to three. Possible shortcomings of the present model are briefly discussed.

When can flexible weak polyelectrolytes be treated as effective rigid objects?

Conformational and ionization equilibria of flexible weak polyelectrolytes (PEs) are, in general, strongly coupled. In this article, we analyze the effect of averaging over (or "contracting") the conformational degrees of freedom so that the original flexible molecule is replaced by an effective rigid object with the same ionization properties. As a result, one obtains the so-called Site Binding (SB) model, much easier to treat both theoretically and computationally, and extensively used to characterize the ionization properties of PE. The conformational averages can be performed in a systematic way by means of the Conformational Contraction Equations (CCEs), which relate the SB parameters to the underlying conformational equilibrium. The conditions for the convergence of the CCE are evaluated in the presence of both Short Range (SR) and Long Range (LR) electrostatic interactions. Two analytically solvable models based on the Freely Jointed Chain (FJC), involving only SR interactions, are analyzed at first. Despite the large chain flexibility, the resulting SB model reproduces the ionization properties with high accuracy. In the case of independent bonds, a very flexible chain can be exactly replaced by an effective rigid object with neighboring pairwise interactions. In general, however, triplet and higher order interactions emerge at the SB level. When LR electrostatic interactions are introduced and combined with the FJC large chain flexibility, the convergence of the CCE for long chains becomes problematic since the SB free energy must be truncated. Similar conclusions are reached for the freely rotating chain and rotational isomeric state models.

Neural network potential for dislocation plasticity in ceramics

Dislocations in ceramics are increasingly recognized for their promising potential in applications such as toughening intrinsically brittle ceramics and tailoring functional properties. However, the atomistic simulation of dislocation plasticity in ceramics remains challenging due to the complex interatomic interactions characteristic of ceramics, which include a mix of ionic and covalent bonds, and highly distorted and extensive dislocation core structures within complex crystal structures. These complexities exceed the capabilities of empirical interatomic potentials. Therefore, constructing neural network potentials (NNPs) emerges as the optimal solution. Yet, creating a training dataset that includes dislocation structures proves difficult due to the complexity of their core configurations in ceramics and the computational demands of density functional theory for large atomic models containing dislocation cores. In this work, we propose a training dataset from properties that are easier to compute via high-throughput calculation. Using this dataset, we have successfully developed NNPs for dislocation plasticity in ceramics, specifically for three typical functional ceramics: ZnO, GaN, and SrTiO3. These NNPs effectively capture the nonstoichiometric and charged core structures and slip barriers of dislocations, as well as the long-range electrostatic interactions between charged dislocations. The effectiveness of this dataset was further validated by measuring the similarity and uncertainty across snapshots derived from large-scale simulations, alongside extensive validation across various properties. Utilizing the constructed NNPs, we examined dislocation plasticity in ceramics through nanopillar compression and nanoindentation, which demonstrated excellent agreement with experimental observations. This study provides an effective framework for constructing NNPs that enable the detailed atomistic modeling of dislocation plasticity, opening new avenues for exploring the plastic behavior of ceramics.

Sequence disorder-induced first order phase transition in confined polyelectrolytes.

We consider a statistical mechanical model of a generic flexible polyelectrolyte, comprised of identically charged monomers with long-range electrostatic interactions and short-range interactions quantified by a disorder field along the polymer contour sequence, which is randomly quenched. The free energy and the monomer density profile of the system for no electrolyte screening are calculated in the case of a system composed of two infinite planar bounding surfaces with an intervening oppositely charged polyelectrolyte chain. We show that the effect of the contour sequence disorder, mediated by short-range interactions, leads to an enhanced localization of the polyelectrolyte chain and a first order phase transition at a critical value of the inter-surface spacing. This phase transition results in an abrupt change of the pressure from negative to positive values, effectively eliminating polyelectrolyte mediated bridging attraction.

Structural analysis of the TPI-Manchester, a thermolabile variant of human triosephosphate isomerase

Human triosephosphate isomerase G122R, also known as TPI-Manchester, is a thermolabile variant detected in a screening of more than 3400 individuals from a population in Ann Arbor, Michigan. Here, the crystallographic structure of G122R was solved to determine the molecular basis of its thermal stability. Structural analysis revealed an increase in the flexibility of residues at the dimer interface, even though R122 is about 20 Å away, suggesting that long-range electrostatic interactions may play a key role in the mutation effect.

Unraveling the Origin of the Repulsive Interaction between Hydrogen Adsorbates on Platinum Single-Crystal Electrodes.

Hydrogen adsorption on platinum (Pt) single-crystal electrodes has been studied intensively in both experiments and computations. Yet, the precise origin and nature of the repulsive interactions observed between hydrogen adsorbates (Hads) have remained elusive. Here, we use first-principles density functional theory calculations to investigate in detail the interactions between Hads on Pt(111), Pt(100), and Pt(110) surfaces. The repulsive interaction between Hads on Pt(111) is deconvoluted into three different physical contributions, namely, (i) electrostatic interactions, (ii) surface distortion effect, and (iii) surface coordination effect. The long-range electrostatic interaction, which is generally considered the most important source of repulsive interactions in surface adsorption, was found to contribute less than 30% of the overall repulsive interaction. The remaining >70% arises from the other two contributions, underscoring the critical influence of surface-mediated interactions on the adsorption process. Surface distortion and coordination effects are found to strongly depend on the coverage and adsorption geometry: the effect of surface distortion dominates when adsorbates reside two or more Pt atoms apart; the effect of surface coordination dominates if hydrogen is adsorbed on neighboring adsorption sites. The above effects are considerably less pronounced on Pt(100) and Pt(110), therefore resulting in weaker interactions between Hads on these two surfaces. Overall, the study highlights the relevance of surface-mediated effects on adsorbate-adsorbate interactions, such as the often-overlooked surface distortion. The effect of these interactions on the hotly debated adsorption site for the adsorbed hydrogen intermediate in the hydrogen evolution reaction is also discussed.

Theory of electrotuneable mechanical force of solid-liquid interfaces: A self-consistent treatment of short-range van der Waals forces and long-range electrostatic forces.

Elucidating the mechanical forces between two solid surfaces immersed in a communal liquid environment is crucial for understanding and controlling adhesion, friction, and electrochemistry in many technologies. Although traditional models can adequately describe long-range mechanical forces, they require substantial modifications in the nanometric region where electronic effects become important. A hybrid quantum-classical model is employed herein to investigate the separation-dependent disjoining pressure between two metal surfaces immersed in an electrolyte solution under potential control. We find that the pressure between surfaces transits from a long-range electrostatic interaction, attractive or repulsive depending on the charging conditions of surfaces, to a strong short-range van der Waals attraction and then an even strong Pauli repulsion due to the redistribution of electrons. The underlying mechanism of the transition, especially the attractive-repulsive one in the short-range region, is elucidated. This work contributes to the understanding of electrotunable friction and lubrication in a liquid environment.

Parallel Brownian dynamics implementation of the Angular Averaged Ewald sums in Coulombic fluids

In nature, Coulombic fluids are present in many different forms, for instance, as aqueous electrolytes dissolving charged biomolecules (such as the DNA or lipid bilayers), macroion or nanoparticle solutions, molten salts, ionic liquids, plasmas, etc. One important characteristic of charged fluids is the long-range electrostatic interaction, which must be incorporated adequately in computer simulations in order to avoid artifacts. In this work, an MPI-Fortran 90 parallel implementation of Coulombic interactions using the Angular Averaged Ewald sums in Brownian dynamics is reported. This computational implementation is considerably faster than the corresponding serial version for a small to a sizable number of particles (tens of thousands). The accuracy of the parallelized Angular Averaged Ewald sums is compared regarding the Ewald sums method at the level of structural and electrostatic properties of a size- and valence-asymmetric electrolyte.

Charge Relaying within a Phospho-Motif Rescue Binding Competency of a Disordered Transcription Factor.

Structural disorder in proteins is central to cellular signaling, where conformational plasticity equips molecules to promiscuously interact with different partners. By engaging with multiple binding partners via the rearrangement of its three helices, the nuclear coactivator binding domain (NCBD) of the CBP/p300 transcription factor is a paradigmatic example of promiscuity. Recently, molecular simulations and experiments revealed that, through the establishment of long-range electrostatic interactions, intended as salt-bridges formed between the post-translationally inserted phosphate and positively charged residues in helix H3 of NCBD, phosphorylation triggers NCBD compaction, lowering its affinity for binding partners. By means of extensive molecular simulations, we here investigated the effect of short-range electrostatics on the conformational ensemble of NCBD, by monitoring the interactions between a phosphorylated serine and conserved positively charged residues within the NCBD phospho-motif. We found that empowering proximal electrostatic interactions, as opposed to long-range electrostatics, can reshape the NCBD ensemble rescuing the binding competency of phosphorylated NCBD. Given the conservation of positive charges in phospho-motifs, proximal electrostatic interactions might dampen the effects of phosphorylation and act as a relay to regulate phosphorylated intrinsically disordered proteins, ultimately tuning the binding affinity for different cellular partners.

Efficient exact exchange using Wannier functions and other related developments in planewave-pseudopotential implementation of RT-TDDFT.

The plane-wave pseudopotential (PW-PP) formalism is widely used for the first-principles electronic structure calculation of extended periodic systems. The PW-PP approach has also been adapted for real-time time-dependent density functional theory (RT-TDDFT) to investigate time-dependent electronic dynamical phenomena. In this work, we detail recent advances in the PW-PP formalism for RT-TDDFT, particularly how maximally localized Wannier functions (MLWFs) are used to accelerate simulations using the exact exchange. We also discuss several related developments, including an anti-Hermitian correction for the time-dependent MLWFs (TD-MLWFs) when a time-dependent electric field is applied, the refinement procedure for TD-MLWFs, comparison of the velocity and length gauge approaches for applying an electric field, and elimination of long-range electrostatic interaction, as well as usage of a complex absorbing potential for modeling isolated systems when using the PW-PP formalism.

Enhanced ethanol dehydration performance of cationic COFs filled anionic alginate hybrid membranes

Adding charged fillers to incorporate long-range electrostatic interactions is an effective way to reduce structure defects and improve separation performance of polyelectrolyte membranes. In this study, three cationic covalent organic frameworks (COFs), TpEB, TpTGCl, and TpPy, were incorporated into anionic alginate to prepare hybrid membranes for pervaporation dehydration of ethanol. With the increase of electrostatic interaction, the decrease in the mobility of polymer chains leads to an increase in the crystallinity and selectivity. Meanwhile, the inherent hydrophilic channels of iCOFs provided additional mass transfer channels, increasing the membrane permeability. When separating a 90 wt% ethanol/water mixture at 76 °C, the hybrid membrane containing 15 wt% TpEB exhibited the highest separation performance with a permeation flux of 2460 g m-2 h-1 and a separation factor of 2110, and it also demonstrated ideal long-term stability. This work provides a new perspective for the selection of fillers in the design process of hybrid membranes in the future.

Peptides and metal ions: A successful marriage for developing artificial metalloproteins.

The mutual relationship between peptides and metal ions enables metalloproteins to have crucial roles in biological systems, including structural, sensing, electron transport, and catalytic functions. The effort to reproduce or/and enhance these roles, or even to create unprecedented functions, is the focus of protein design, the first step toward the comprehension of the complex machinery of nature. Nowadays, protein design allows the building of sophisticated scaffolds, with novel functions and exceptional stability. Recent progress in metalloprotein design has led to the building of peptides/proteins capable of orchestrating the desired functions of different metal cofactors. The structural diversity of peptides allows proper selection of first- and second-shell ligands, as well as long-range electrostatic and hydrophobic interactions, which represent precious tools for tuning metal properties. The scope of this review is to discuss the construction of metal sites in de novo designed and miniaturized scaffolds. Selected examples of mono-, di-, and multi-nuclear binding sites, from the last 20 years will be described in an effort to highlight key artificial models of catalytic or electron-transfer metalloproteins. The authors' goal is to make readers feel like guests at the marriage between peptides and metal ions while offering sources of inspiration for future architects of innovative, artificial metalloproteins.

Modelling iodine diffusion in 2D-Perovskites as a function of the length of the organic spacer molecules

Two-dimensional (2D) hybrid perovskites have emerged as promising materials for solar cell applications, offering increased stability and reduced ionic movements compared to the more commonly used 3D-perovskites. The atomistic mechanisms behind the lower ionic diffusion in these perovskites have remained rather underexplored. In this study, we have used density functional theory (DFT) to investigate the potential tunability of ionic movements in 2D-perovskites by varying the spacer molecules in the structure. We demonstrate that by changing the length of the spacer molecule, and thus the distance between the two-dimensional inorganic slabs of corner sharing lead halide octahedra, it is possible to significantly modulate the periodic potential distribution and long-range electrostatic interactions within these materials. This modulation has a distinct impact on the energy barriers for ionic movement. Notably, we find that longer spacer molecules decrease the energy barrier for in-plane iodine diffusion, while increasing the energy barrier for inter-layer diffusion. Furthermore, we observe a logarithmic relationship between the energy barrier for inter-layer diffusion and the inter-layer distance. These results clarify the mechanisms underlying the lower ionic movement in 2D-perovskites and open avenues for engineering ionic migration in novel 2D-perovskite structures, thus enhancing their applicability in optoelectronic technologies.

Towards atomistic modelling of solid Pb-O formation and dissolution in liquid lead coolant: Interatomic potential development

Microscopic description of solid Pb-O formation and dissolution in liquid lead coolant is important for the modelling of fast neutron reactors. For this purpose, in this work we develop interatomic potential models for lead melt with dissolved oxygen. Potentials fitting is based on ab initio density functional theory (DFT) calculations and quantum molecular dynamics. Two different potential models are trained on the DFT data using the force-matching method: a computationally cheap embedded atom method (EAM) potential and a computationally expensive machine-learned moment tensor potential (MTP) that allows reproducing DFT data very accurately. The potentials are further validated to reproduce the properties of lead melt and the coordination of oxygen atoms in molten lead. The diffusion coefficient for oxygen in molten lead is calculated and compared with the available experimental data. We analyse how these interatomic potential models describe the crystal structure of the solid lead oxide to consider PbO nucleation and dissolution in molten lead. We extensively discuss the arising problems with the description of long-range electrostatic interactions. The possibility of matching the experimental data on the oxygen solubility limit by tuning the EAM potential is shown.

From proton transfer to ionic liquids: How isolated domains of ion pairs grow to form extended network in diphenyl phosphate-bis(2-ethylhexyl) amine mixtures

HypothesisDiphenyl phosphate (DPP)/bis(2-ethylhexyl) amine (BEEA) liquid mixtures are expected to show peculiar self-assembly behavior due to their, respectively, acidic and basic nature which can trigger acid-base reaction. ExperimentThe properties of DPP/BEEA mixtures of different compositions are explored by a combined theoretical (Ab initio calculations) and experimental (rheology, X-ray and light scattering) approach. Findings(i) a proton transfer from DPP to BEEA takes place with formation of cationic and anionic species in liquid phase (ion pair formation); (ii) due to the strong and long-range electrostatic interactions, each of the two charged species is preferentially surrounded by the other one, in a picture resembling the structure of ionic materials; (iii) this gives rise to a striking viscosity increase takes place as DPP concentration increases; (iv) the composition dependencies of all measured parameters show clear deviations around DPP molar fraction of 0.2, indicating unequivocally the formation of isolated domains, formed by DPP-BEEA pairs which coalescence/percolate into a unique, extended, network at higher DPP concentrations.

Incorporating long-range electrostatics in neural network potentials via variational charge equilibration from shortsighted ingredients

We present a new approach to construct machine-learned interatomic potentials including long-range electrostatic interactions based on a charge equilibration scheme. This new approach can accurately describe the potential energy surface of systems with ionic and covalent interactions as well as systems with multiple charge states. Moreover, it can either be regressed against known atomic charge decompositions or trained without charge targets, without compromising the accuracy of energy and forces. We benchmark our approach against other state-of-the-art models and prove it to have equivalent performances on a set of simple reference systems while being less computationally expensive. Finally, we demonstrate the accuracy of our approach on complex systems: solid and liquid state sodium chloride. We attain accuracy in energy and forces better than the model based on local descriptors and show that our electrostatic approach can capture the density functional theory tail of the potential energy surface of the isolated Na-Cl dimer, which the local descriptor-based model fails to describe.

Implementing the spherical grids and treecode algorithm for efficient parallelization of long-range electrostatic interactions

Implementing the spherical grids and treecode algorithm for efficient parallelization of long-range electrostatic interactions

Effect of histidine protonation state on ligand binding at the ATP-binding site of human protein kinase CK2

Histidine residues contribute to numerous molecular interactions, owing to their structure with the ionizable aromatic side chain with pKa close to the physiological pH. Herein, we studied how the two histidine residues, His115 and His160 of the catalytic subunit of human protein kinase CK2, affect the binding of the halogenated heterocyclic ligands at the ATP-binding site. Thermodynamic studies on the interaction between five variants of hCK2α (WT protein and four histidine mutants) and three ionizable bromo-benzotriazoles and their conditionally non-ionizable benzimidazole counterparts were performed with nanoDSF, MST, and ITC. The results allowed us to identify the contribution of interactions involving the particular histidine residues to ligand binding. We showed that despite the well-documented hydrogen bonding/salt bridge formation dragging the anionic ligands towards Lys68, the protonated His160 also contributes to the binding of such ligands by long-range electrostatic interactions. Simultaneously, His 115 indirectly affects ligand binding, placing the hinge region in open/closed conformations.

Ligand coupling mechanism of the human serotonin transporter differentiates substrates from inhibitors

The presynaptic serotonin transporter (SERT) clears extracellular serotonin following vesicular release to ensure temporal and spatial regulation of serotonergic signalling and neurotransmitter homeostasis. Prescription drugs used to treat neurobehavioral disorders, including depression, anxiety, and obsessive-compulsive disorder, trap SERT by blocking the transport cycle. In contrast, illicit drugs of abuse like amphetamines reverse SERT directionality, causing serotonin efflux. Both processes result in increased extracellular serotonin levels. By combining molecular dynamics simulations with biochemical experiments and using a homologous series of serotonin analogues, we uncovered the coupling mechanism between the substrate and the transporter, which triggers the uptake of serotonin. Free energy analysis showed that only scaffold-bound substrates could initiate SERT occlusion through attractive long-range electrostatic interactions acting on the bundle domain. The associated spatial requirements define substrate and inhibitor properties, enabling additional possibilities for rational drug design approaches.

PMC-IZ: A Simple Algorithm for the Electrostatics Calculation in Slab Geometric Molecular Dynamics Simulations.

Slab geometric systems are widely utilized in molecular simulations. However, an efficient, straightforward, and accurate method for calculating electrostatic interactions in these systems for molecular dynamics (MD) simulations is still needed. This review introduces a PME-like approach called PMC-IZ, specifically designed for slab geometric systems. Traditional approaches for long-range electrostatic interaction calculations in slab geometry typically involve Ewald summation, where the Gaussian charge density is summed within 3D unit cells and then integrated in the 2D periodic space. In the proposed approach here, the Poisson equation was solved for a single Gaussian charge density within 2Dl periodic space, followed by convolution within 3D unit cells using an effective potential as the convolution kernel for summation. The effective potential ensures that the solution within the region of interest adheres strictly to 2D periodic boundary conditions while inherently possessing 3D periodic boundary condition properties. The PMC-IZ method provides for such systems accurate treatment of electrostatic interactions, overcomes limitations associated with finite vacuum layers, and offers improved computational efficiency. We thus postulate that this method provides a valuable tool for studying electrostatic interactions in slab geometric system MD simulations. It has promising applications in various areas such as surface science, catalysis, and materials research, where accurate modeling of slab geometric electrostatic interactions is essential.