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Biased and accelerated molecular simulations (BAMS) are widely used tools to observe relevant molecular phenomena occurring on time scales inaccessible to standard molecular dynamics, but evaluation of the physical time scales involved in the processes is not directly possible from them. For this reason, the problem of recovering dynamics from such kinds of simulations is the object of very active research due to the relevant theoretical and practical implications of dynamics on the properties of both natural and synthetic molecular systems. In a recent paper [A. Rapallo et al., J. Comput. Chem. 42, 586–599 (2021)], it has been shown how the coupling of BAMS (which destroys the dynamics but allows to calculate average properties) with Extended Diffusion Theory (EDT) (which requires input appropriate equilibrium averages calculated over the BAMS trajectories) allows to effectively use the Smoluchowski equation to calculate the orientational time correlation function of the head–tail unit vector defined over a peptide in water solution. Orientational relaxation of this vector is the result of the coupling of internal molecular motions with overall molecular rotation, and it was very well described by correlation functions expressed in terms of weighted sums of suitable time-exponentially decaying functions, in agreement with a Brownian diffusive regime. However, situations occur where exponentially decaying functions are no longer appropriate to capture the actual dynamical behavior, which exhibits persistent long time correlations, compatible with the so called subdiffusive regimes. In this paper, a generalization of EDT will be given, exploiting a fractional Smoluchowski equation (FEDT) to capture the non-exponential character observed in the relaxation of intramolecular distances and molecular radius of gyration, whose dynamics depend on internal molecular motions only. The calculation methods, proper to EDT, are adapted to implement the generalization of the theory, and the resulting algorithm confirms FEDT as a tool of practical value in recovering dynamics from BAMS, to be used in general situations, involving both regular and anomalous diffusion regimes. [ABSTRACT FROM AUTHOR]

Liquid–liquid phase transitions hold a unique and profound significance within condensed matter physics. These transitions, while conceptually intriguing, often pose formidable computational challenges. However, recent advances in neural network (NN) potentials offer a promising avenue to effectively address these challenges. In this paper, we delve into the structural transitions of liquid CdTe, CdS, and their alloy systems using molecular dynamics simulations, harnessing the power of an NN potential named LaspNN. Our investigations encompass both pressure and temperature effects. Through our simulations, we uncover three primary liquid structures around melting points that emerge as pressure increases: tetrahedral, rock salt, and close-packed structures, which greatly resemble those of solid states. In the high-temperature regime, we observe the formation of Te chains and S dimers, providing a deeper understanding of the liquid's atomic arrangements. When examining CdSxTe1−x alloys, our findings indicate that a small substitution of S by Te atoms for S-rich alloys (x > 0.5) exhibits a structural transition much different from CdS, while a large substitution of Te by S atoms for Te-rich alloys (x < 0.5) barely exhibits a structural transition similar to CdTe. We construct a schematic diagram for liquid alloys that considers both temperature and pressure, providing a comprehensive overview of the alloy system's behavior. The local aggregation of Te atoms demonstrates a linear relationship with alloy composition x, whereas that of S atoms exhibits a nonlinear one, shedding light on the composition-dependent structural changes. [ABSTRACT FROM AUTHOR]

Understanding how external electric fields (EFs) impact the properties of aqueous molecules is crucial for various applications in chemistry, biology, and engineering. In this paper, we present a study utilizing molecular dynamics simulation to explore how direct-current (DC) and alternative-current (AC) EFs affect hydrophobic (n-triacontane) and hydrophilic (PEG-10) oligomer chains. Through a machine learning approach, we extract a 2-dimensional free energy (FE) landscape of these molecules, revealing that electric fields modulate the FE landscape to favor stretched configurations and enhance the alignment of the chain with the electric field. Our observations indicate that DC EFs have a more prominent impact on modulation compared to AC EFs and that EFs have a stronger effect on hydrophobic chains than on hydrophilic oligomers. We analyze the orientation of water dipole moments and hydrogen bonds, finding that EFs align water molecules and induce more directional hydrogen bond networks, forming 1D water structures. This favors the stretched configuration and alignment of the studied oligomers simultaneously, as it minimizes the disruption of 1D structures. This research deepens our understanding of the mechanisms by which electric fields modulate molecular properties and could guide the broader application of EFs to control other aqueous molecules, such as proteins or biomolecules. [ABSTRACT FROM AUTHOR]

It is significant to investigate the calcium carbonate (CaCO3) precipitation mechanism during the carbon capture process; nevertheless, CaCO3 precipitation is not clearly understood yet. Understanding the carbonation mechanism at the atomic level can contribute to the mineralization capture and utilization of carbon dioxide, as well as the development of new cementitious materials with high-performance. There are many factors, such as temperature and CO2 concentration, that can influence the carbonation reaction. In order to achieve better carbonation efficiency, the reaction conditions of carbonation should be fully verified. Therefore, based on molecular dynamics simulations, this paper investigates the atomic-scale mechanism of carbonation. We investigate the effect of carbonation factors, including temperature and concentration, on the kinetics of carbonation (polymerization rate and activation energy), the early nucleation of calcium carbonate, etc. Then, we analyze the local stresses of atoms to reveal the driving force of early stage carbonate nucleation and the reasons for the evolution of polymerization rate and activation energy. Results show that the higher the calcium concentration or temperature, the higher the polymerization rate of calcium carbonate. In addition, the activation energies of the carbonation reaction increase with the decrease in calcium concentrations. [ABSTRACT FROM AUTHOR]

In this paper, we present a combined experimental and theoretical study that explored the initial sticking of water on cooled surfaces. Specifically, these ultra-high vacuum gas–surface scattering experiments utilized supersonic molecular beam techniques in conjunction with a cryogenically cooled highly oriented pyrolytic graphite crystal, giving control over incident kinematic conditions. The D2O translational energy spanning 300–750 meV, the relative D2O flux, and the incident angle could all be varied independently. Three different experimental measurements were made. One involved measuring the total amount of D2O scattering as a function of surface temperature to determine the onset of sticking under non-equilibrium gas–surface collision conditions. Another measurement used He specular scattering to assess structural and coverage information for the interface during D2O adsorption. Finally, we used time-of-flight (TOF) measurements of the scattered D2O to determine how energy is exchanged with the graphite surface at surface temperatures above and near the conditions needed for gaseous condensation. For comparison and elaboration of the roles that internal degrees of freedom play in this process, we also did similar TOF measurements using another mass 20 incident particle, atomic neon. Enriching this study are precise molecular dynamics simulations that elaborate on gas–surface energy transfer and the roles of molecular degrees of freedom in gas–surface collisional energy exchange processes. This study furthers our fundamental understanding of energy exchange and the onset of sticking and ultimately gaseous condensation for gas–surface encounters occurring under high-velocity flows. [ABSTRACT FROM AUTHOR]

This paper reports on the effects of shear rate and interface modeling parameters on the hydrodynamic slip length (LS) for water–graphite interfaces calculated using non-equilibrium molecular dynamics. Five distinct non-bonded solid–liquid interaction parameters were considered to assess their impact on LS. The interfacial force field derivations included sophisticated electronic structure calculation-informed and empirically determined parameters. All interface models exhibited a similar and bimodal LS response when varying the applied shear rate. LS in the low shear rate regime (LSR) is in good agreement with previous calculations obtained through equilibrium molecular dynamics. As the shear rate increases, LS sharply increases and asymptotes to a constant value in the high shear regime (HSR). It is noteworthy that LS in both the LSR and HSR can be characterized by the density depletion length, whereas solid–liquid adhesion metrics failed to do so. For all interface models, LHSR calculations were, on average, ∼28% greater than LLSR, and this slip jump was confirmed using the SPC/E and TIP4P/2005 water models. To address the LS transition from the LSR to the HSR, the viscosity of water and the interfacial friction coefficient were investigated. It was observed that in the LSR, the viscosity and friction coefficient decreased at a similar rate, while in the LSR-to-HSR transition, the friction coefficient decreased at a faster rate than the shear viscosity until they reached a new equilibrium, hence explaining the LS-bimodal behavior. This study provides valuable insights into the interplay between interface modeling parameters, shear rate, and rheological properties in understanding hydrodynamic slip behavior. [ABSTRACT FROM AUTHOR]

Several enhanced sampling techniques rely on the definition of collective variables to effectively explore free energy landscapes. The existing variables that describe the progression along a reactive pathway offer an elegant solution but face a number of limitations. In this paper, we address these challenges by introducing a new path-like collective variable called the "deep-locally non-linear-embedding," which is inspired by principles of the locally linear embedding technique and is trained on a reactive trajectory. The variable mimics the ideal reaction coordinate by automatically generating a non-linear combination of features through a differentiable generalized autoencoder that combines a neural network with a continuous k-nearest neighbor selection. Among the key advantages of this method is its capability to automatically choose the metric for searching neighbors and to learn the path from state A to state B without the need to handpick landmarks a priori. We demonstrate the effectiveness of DeepLNE by showing that the progression along the path variable closely approximates the ideal reaction coordinate in toy models, such as the Müller-Brown potential and alanine dipeptide. Then, we use it in the molecular dynamics simulations of an RNA tetraloop, where we highlight its capability to accelerate transitions and estimate the free energy of folding. [ABSTRACT FROM AUTHOR]

Protein–calixarenes binding plays an increasingly central role in many applications, spanning from molecular recognition to drug delivery strategies and protein inhibition. These ligands obey a specific bio-supramolecular chemistry, which can be revealed by computational approaches, such as molecular dynamics simulations. In this paper, we rely on all-atom, explicit-solvent molecular dynamics simulations to capture the electrostatically driven association of a phosphonated calix-[4]-arene with cytochome-C, which critically relies on surface-exposed paired lysines. Beyond two binding sites identified in direct agreement with the x-ray structure, the association has a larger structural impact on the protein dynamics. Then, our simulations allow a direct comparison to analogous calixarenes, namely, sulfonato, similarly reported as "molecular glue." Our work can contribute to a robust in silico predictive tool to assess binding sites for any given protein of interest for crystallization, with the specificity of a macromolecular cage whose endo/exo orientation plays a role in the binding. [ABSTRACT FROM AUTHOR]

In order to prepare highly heat-resistant packaging insulation materials, in this paper, bismaleimide/epoxy resin (BMI/EP55) composites with different contents of BMI were prepared by melt blending BMI into amino tetrafunctional and phenolic epoxy resin (at a ratio of 5:5). The microstructures and thermal and electrical properties of the composites were tested. The electrostatic potential distribution, energy level distribution, and molecular orbitals of BMI were calculated using Gaussian. The results showed that the carbonyl group in BMI is highly electronegative, implying that the carbonyl group has a strong electron trapping ability. The thermal decomposition temperature of the composites gradually increased with the increase of BMI content, and the 20% BMI/EP55 composites had the highest heat-resistance index, along with a glass transition temperature (Tg) of >250 °C. At different test temperatures, with increase in the BMI content, the conductivity of epoxy resin composites showed a tendency to first decrease and then increase, the breakdown field strength showed a tendency to first increase and then decrease, and the dielectric constant was gradually decreased. Two trap centers were present simultaneously in the composites, where the shallow trap energy level is the deepest in 20% BMI/EP composites and the deep trap energy level is the deepest in 10% BMI/EP55 composites. Correspondingly, the 10% BMI/EP55 composite had a slower charge decay rate, while the 20% BMI/EP55 had a faster charge decay rate. In summary, the BMI/EP55 composites with high heat resistance and insulating properties were prepared in this study, which provided ideas for preparing high-temperature packaging insulating materials. [ABSTRACT FROM AUTHOR]

The large discrepancy among the nucleation kinetics extracted from experimental measurements and computer simulations and the prediction of the classical nucleation theory (CNT) has stimulated intense arguments about its origin in the past decades, which is crucially relevant to the validity of the CNT. In this paper, we investigate the atomistic mechanism of the nucleation in liquid Al in contact with amorphous substrates with atomic-level smooth/rough surfaces, using molecular dynamics (MD) simulations. This study reveals that the slightly distorted local fcc/hcp structures in amorphous substrates with smooth surfaces can promote heterogeneous nucleation through a structural templating mechanism, and on the other hand, homogeneous nucleation will occur at a larger undercooling through a fluctuation mechanism if the surface is rough. Thus, some impurities, previously thought to be impotent, could be activated in the homogeneous nucleation experiments. We further find that the initial growth of the nucleus on smooth surfaces of amorphous substrates is one order of magnitude faster than that in homogeneous nucleation. Both these factors could significantly contribute to the discrepancy in the nucleation kinetics. This study is also supported by a recent study of the synthesis of high-entropy alloy nanoparticles assisted with the liquid metal Ga [Cao et al., Nature 619, 73 (2023)]. In this study, we established that the boundary existed between homogeneous and heterogeneous nucleation, i.e., the structural templating is a general mechanism for heterogeneous nucleation, and in its absence, homogeneous nucleation will occur through the fluctuation mechanism. This study provides an in-depth understanding of the nucleation theory and experiments. [ABSTRACT FROM AUTHOR]

Hydration and, in particular, the coordination number of a metal ion is of paramount importance as it defines many of its (bio)physicochemical properties. It is not only essential for understanding its behavior in aqueous solutions but also determines the metal ion reference state and its binding energy to (bio)molecules. In this paper, for divalent metal cations Ca 2 + , Cd 2 + , Cu 2 + , Fe 2 + , Hg 2 + , Mg 2 + , Ni 2 + , Pb 2 + , and Zn 2 + , we compare two approaches for predicting hydration numbers: (1) a mixed explicit/continuum DFT-D3//COSMO-RS solvation model and (2) density functional theory based ab initio molecular dynamics. The former approach is employed to calculate the Gibbs free energy change for the sequential hydration reactions, starting from [M( H 2 O)2]2+ aqua complexes to [M( H 2 O) 9]2+, allowing explicit water molecules to bind in the first or second coordination sphere and determining the most stable [M( H 2 O) n]2+ structure. In the latter approach, the hydration number is obtained by integrating the ion–water radial distribution function. With a couple of exceptions, the metal ion hydration numbers predicted by the two approaches are in mutual agreement, as well as in agreement with the experimental data. [ABSTRACT FROM AUTHOR]

A Markov state model is a powerful tool that can be used to track the evolution of populations of configurations in an atomistic representation of a protein. For a coarse-grained linear chain model with discontinuous interactions, the transition rates among states that appear in the Markov model when the monomer dynamics is diffusive can be determined by computing the relative entropy of states and their mean first passage times, quantities that are unchanged by the specification of the energies of the relevant states. In this paper, we verify the folding dynamics described by a diffusive linear chain model of the crambin protein in three distinct solvent systems, each differing in complexity: a hard-sphere solvent, a solvent undergoing multi-particle collision dynamics, and an implicit solvent model. The predicted transition rates among configurations agree quantitatively with those observed in explicit molecular dynamics simulations for all three solvent models. These results suggest that the local monomer–monomer interactions provide sufficient friction for the monomer dynamics to be diffusive on timescales relevant to changes in conformation. Factors such as structural ordering and dynamic hydrodynamic effects appear to have minimal influence on transition rates within the studied solvent densities. [ABSTRACT FROM AUTHOR]

The network structure within polymers significantly influences their mechanical properties, including their strength, toughness, and fatigue resistance. All-atom molecular dynamics (AAMD) simulations offer a method to investigate the energy dissipation mechanism within polymers during deformation and fracture; Such an approach is, however, computationally inefficient when used to analyze polymers with complex network structures, such as the common chemically double-networked hydrogels. Alternatively, coarse-grained molecular dynamics (CGMD) models, which reduce the computational degrees of freedom by concentrating a set of adjacent atoms into a coarse-grained bead, can be employed. In CGMD simulations, a coarse-grained force field (CGFF) is a critical factor affecting the simulation accuracy. In this paper, we proposed a data-based method for predicting the CGFF parameters to improve the simulation efficiency of complex cross-linked network in polymers. Here, we utilized a typical chemically double-networked hydrogel as an example. An artificial neural network was selected, and it was trained with the tensile stress–strain data from the CGMD simulations using different CGFF parameters. The CGMD simulations using the predicted CGFF parameters show good agreement with the AAMD simulations and are almost fifty times faster. The data-inspired CGMD model presented here broadens the applicability of molecular dynamics simulations to cross-linked polymers and has the potential to provide insights that will aid the design of polymers with desirable mechanical properties. [ABSTRACT FROM AUTHOR]