Force Field Research Articles

The dissolution of metal carbonate precipitation under the external electric and magnetic fields: reactive force field molecular dynamics simulation

Abstract The dissolution of metal carbonates holds a pivotal role in diverse industrial processes, environmental occurrences, and geological formations. Grasping the fundamental mechanisms underlying these processes is imperative for enhancing industrial applications and mitigating environmental impacts. Herein, we undertake a thorough investigation employing reactive forcefieldmolecular dynamics simulations to delve into the dissolution process of metal carbonates. These simulations afford profound insights into the mechanisms and kinetics governing the process across various conditions, encompassing temperature, acidity, and external electric and magnetic fields. Although temperature itself exerts a limited influence, the study reveals a synergistic enhancement of metal carbonate dissolution kinetics when temperature is combined with static electric and magnetic fields. Our revelations offer enlightening perspectives on the intricate interplay of factors shaping dissolution processes, laying the foundation for future inquiries in this domain.

Cerium single atom anchored silver selenide: A high-performance catalyst for hydrogen evolution reaction with ultra-low activation energy and enhanced stability

Single-atom catalysts (SAC) have enabled high electrocatalytic activity production because of the enormous active sites bearing catalysts for hydrogen evolution reaction (HER). Herein, we report cerium single atom (Cesa) anchored silver selenide (Ag2Se) via metal-chalcogenide interaction for high-performance HER evolution. The designed electrocatalyst possesses extraordinary electrochemical and optical properties, thus the Cesa-Ag2Se could be a potential catalyst for the photo-electrochemical HER process. The sluggish diffusion rate of the Ce atom within the Ag2Se facilitates the high stability and the dispersed Cesa displayed ∼2 eV of energy differences with the Ce dimer proving the formation of dispersed Cesa over Ag2Se is potentially favorable. The minimum energy pathway (MEP) suggested the Cesa-Ag2Se system established only 0.003 eV as an ultra-low activation energy barrier in order to produce H2, which is by far the finest performance of Ag2Se-based thermodynamically favourable catalysis. The accumulated electron density due to the presence of Cesa enabled the presence of a large number of potential active sites (Ce and Ag) for hydrogen adsorption, in addition, the supportive zero band gap of the Ag2Se accelerated the kinetics for HER. Ultimately, we employed the Wentzcovitch cell dynamics-based molecular dynamics study for H* adsorbed Cesa-Ag2Se that suggests the stability of the catalyst for 500 fs. The in-depth HER modelling using the Lennard-Jones force field demonstrated the insightful dynamics behaviour of the catalytical system.

Quantification of Anisotropy in Exchange and Dispersion Interactions: A Simple Model for Physics-Based Force Fields.

In some compounds, exchange repulsion is orientation dependent. However, in contrast to quantum chemical methods that treat exchange explicitly, empirical models assume exchange to be spherically symmetric, yielding an average description only. Here we quantify the anisotropy of exchange and dispersion energy for hydrogen halides and water by probing these compounds with a helium atom using the symmetry-adapted perturbation theory (SAPT). The exchange interaction is reduced by up to 33% due to the σ-hole in hydrogen iodide, depending on the location of the probe. We demonstrate how this anisotropy can be modeled in empirical force fields either using an angle-dependent potential or by introducing virtual sites, reducing the error in the empirical model by a factor of 5 compared to isotropic atoms. Lone-pairs on water, positioned close to perpendicular to the plane of the molecule, on a line with the oxygen atom, and, surprisingly, σ-holes on water both modulate the exchange interaction strongly. Both lone-pairs and σ-holes can be modeled by virtual sites, leading to an 80% reduced error.

An application of recurrence relations to central force fields

By generalizing the relationships between auxiliary variables that appear in the works of A.E. Roy, we investigate the dynamics of non-interacting particles under the action of a central force field whose force function is a solution to a certain differential equation. The method used by A.E. Roy, R. Broucke, and later by G. Sitarski, was utilized in such a way that the obtained recursive equations can be used to describe the motion of a particle under such a field. The class of such fields includes both gravitational and non-gravitational force fields. Several numerical and historically essential examples and detailed discussions of various cases are given.

From Graphene Oxide to Graphene: Changes in Interfacial Water Structure and Reactivity Using Deep Neural Network Force Fields

From Graphene Oxide to Graphene: Changes in Interfacial Water Structure and Reactivity Using Deep Neural Network Force Fields

Insight from atomistic molecular dynamics simulations into the supramolecular assembly of the aldo-keto reductase from Trypanosoma cruzi.

Currently, Chagas disease represents an important public health problem affecting more than 8 million people worldwide. The vector of this disease is the Trypanosoma cruzi (Tc) parasite. Our research specifically focuses on the structure and aggregation states of the enzyme aldo-keto reductase of Tc (TcAKR) reported in this parasite. TcAKR belongs to the aldo-keto reductase (AKR) superfamily, enzymes that catalyze redox reactions involved in crucial biological processes. While most AKRs are found in monomeric forms, some have been reported to form dimeric and tetrameric structures. This is the case for some TcAKR. To better understand how TcAKR multimers form and remain stable, we conducted a comprehensive computational analysis using molecular dynamics (MD) simulations. Our approach to elucidating the aggregation states of TcAKR involved two strategies. Initially, we explored the dynamic behaviour of pre-assembled TcAKR dimers. Subsequently, we examined the self-aggregation of eight monomers. This investigation led to the identification of crucial residues that contribute to the stabilization of protein-protein interactions. It was also found that TcAKRs can form stable supramolecular assemblies, with each monomer typically surrounded by three first neighbours. These findings align with experimental reports of tetrameric or more complex supramolecular structures. Our computational studies could guide further experimental investigations aiming at drug development and assist in designing strategies to modulate aggregation. Atomistic molecular dynamics simulations were carried out. The TcAKR 3D model structure was obtained by homology modelling using the Swiss Model for the TcAKR sequence (GenBank accession no. EU558869). Further, we checked the model with Alphafold2 and found a high degree of similarity between models. Several tools were used to build the dimers including CLUSPRO, GRAMM-Docking, Hdock, and Py-dock. Protein superstructures were built using the PACKMOL package. CHARMM-GUI was used to set up the simulation systems. GROMACS version 2020.5 was used to perform the simulations with the CHARMM36 force field for the protein and ions and the TIP3P model for water. Further analyses were performed using VMD, GROMACS, AMBER tools, MDLovoFit, bio3d, and in-house programs.

Capturing Different Intermediate Phases during Zeolite Synthesis.

Despite extensive efforts over the past several decades, the current mechanistic understanding of zeolite crystallization is still far from satisfactory, thus precluding the synthesis of designer zeolites. Here we show that the nucleation and in situ transformation pathways during the synthesis of medium-pore zeolite TNU-9 can be altered by controlling the extent of cooperative structure direction between Na+ and Cs+ ions in the presence of 1,4-bis(N-methylpyrrolidinium)butane cations as an organic structure-directing agent. The intermediate phase selectivity was found to change from bikitaite to analcime to layered MCM-22 precursor when the gel Na/Cs ratio was adjusted to 7, 15, and 20, respectively. We also show that the transformation of bikitaite into TNU-9 begins at the surface of intermediate crystals, unlike that of analcime and MCM-22 precursor by a dissolution-recrystallization process. The force field simulation results suggest that the nucleation of different intermediate phases is not thermodynamically but kinetically controlled. This study provides a new basis for advancing the fundamental understanding of zeolite crystallization pathways.

Theoretical investigation of potential energetic material CL-20/TNBP co-crystal explosive based on molecular dynamics method.

The exploration of CL-20 eutectic has been a subject of fervent interest within the realm of high-energy material modification. Through the utilization of density functional and molecular dynamics methods, an investigation into the characteristics of hexanitrohexaazaisowurtzitane (CL-20)/4,4',5,5'-tetranitro-2H,2'H-3,3'-bipyrazole (TNBP)within the molar ratio range of 4:1-1:4 was conducted. This inquiry encompassed the scrutiny of molecular interaction pathways, attachment force, initiating molecular distance, unified energy concentration, and physical characteristics. Furthermore, the EXPLO-5 was harnessed to prognosticate the explosion features and byproducts of unadulterated CL-20, TNBP, and CL-20/TNBP frameworks. The findings delineate a substantial differentiation in the electrostatic charge distribution on the surface between CL-20 and TNBP particles, signifying the preeminence of intermolecular interactions between disparate entities over those within similar entities, thus intimating the plausibility of the eutectic constitution. Remarkably, the identification of maximal attachment force at a molar ratio of 1:1 suggests the heightened likelihood of eutectic formation, propelled primarily by electrostatic and van der Waals forces. The resultant eutectic explosive evinces intermediate reactivity and exemplary mechanical attributes. Moreover, the detonation achievement of the eutectic with a molar proportion of 1:1 straddles that of CL-20 and TNBP, representing a new type of insensitive high-energy material. The testing method employs the Materials Studio software and utilizes the molecular dynamics (MD) method to predict the properties of CL-20/TNBP cocrystals with different ratios and crystal faces. The MD simulation time step is set to 1fs, and the total MD simulation time is 2ns. An isothermal-isobaric (NPT) ensemble is used for the 2ns MD simulation. The COMPASS force field is employed, with the temperature set to 295K. The prediction of detonation characteristics and products is conducted using the EXPLO-5 software.

Superior Li‐Ion Transport in LiNb0.5Ta0.5Cl6

AbstractHalide‐based solid‐state electrolytes have emerged as promising candidates for all‐solid‐state lithium batteries. Among them, amorphous LiTaCl6 and LiNbCl6 have shown remarkable conductivities at room temperature, up to 11.0 and 13.5 mS cm−1 at 298.15 K, respectively. Surpassing these values, molecular dynamics simulations based on machine‐learning force fields predict that the Li‐ion conductivity in LiNb0.5Ta0.5Cl6 can reach 15.7 mS cm−1 at 298.15 K with an activation energy of 0.146 eV. Li‐ion mobility is found to correlate with the degree of anharmonic cation‐anion coupling: LiNb0.5Ta0.5Cl6 shows the strongest coupling of low‐frequency Li‐ion modes with Cl‐ion vibration modes. Despite the many similarities between Nb and Ta, this work demonstrates that when both are present, the synergy between Nb and Ta can result in significantly enhanced superionic Li‐ion conductivity in LiNb0.5Ta0.5Cl6, surpassing that observed in both LiTaCl6 and LiNbCl6.

Improved GAN-based deep learning approach for strain field prediction and failure analysis of precast bridge slab joints

Nonlinear finite element analysis (FEA) is crucial for understanding complex stress-strain behaviors and assessing potential structural failure risks. Building on the recent success of deep learning (DL) methods in physical field predictions, there has been growing interest in forecasting internal force fields of structures beyond the existing data scope. In this study, a data-driven strain field analysis model based on an improved Generative Adversarial Network (GAN) is proposed. To enrich the input data, this novel approach incorporates the Signed Distance Field (SDF) derived from the characteristics of the original Finite Element (FE) models. The generator, based on U-Net, is enhanced with channel attention mechanisms and loss penalty terms, forming the core of the improved GAN. The effectiveness of the proposed method is demonstrated using two parallel datasets comprising 3-Headed bar tension models across two planes. Subsequently, the generated strain field is subjected to feature extraction using the 2D-Principal Component Analysis (2D-PCA) method, followed by structural failure determination through the K-Nearest Neighbor (KNN) algorithm. The results indicate that the proposed improved approach achieves a significant reduction in Root Mean Square Error (RMSE) of 20.71 % and 13.21 % across the two datasets, with the structural failure determination method reaching an F1-score of 0.971. Thus, the intelligent strain field analysis method introduced here effectively predicts strain distributions and promptly identifies failure states, offering valuable insights for further numerical analysis and structural design.

Modelling DNA-ligand interactions through variable Force Field based MD Simulations

Modelling DNA-ligand interactions through variable Force Field based MD Simulations

Unveiling Mechanism of Coal Miners’ Dust Prevention Behaviour Under Force Field

Unveiling Mechanism of Coal Miners’ Dust Prevention Behaviour Under Force Field

Routes to Bidirectional Cathodes for Reversible Aprotic Alkali Metal–CO2 Batteries

AbstractAprotic alkali metal–CO2 batteries (AAMCBs) have garnered significant interest owing to fixing CO2 and providing large energy storage capacity. The practical implementation of AAMCBs is constrained by the sluggish kinetics of the CO2 reduction reaction (CO2RR) and the CO2 evolution reaction (CO2ER). Because the CO2ER and CO2RR take place on the cathode, which connects the internal catalyst with the external environment. Building a bidirectional cathode with excellent CO2ER and CO2RR kinetics by optimizing the cathode's internal catalyst and environment has attracted most of the attention to improving the electrochemical performance of AAMCBs. However, there remains a lack of comprehensive understanding. This review aims to give a route to bidirectional cathodes for reversible AAMCBs, by systematically discussing engineering strategies of both the internal catalyst (atomic, nanoscopic, and macroscopic levels) and the external environment (photo, photo‐thermal, and force field). The CO2ER and CO2RR mechanisms and the “engineering strategies from internal catalyst to the external environment–cathode properties–CO2RR and CO2ER kinetics and mechanisms–batteries performance” relationship are elucidated by combining computational and experimental approaches. This review establishes a fundamental understanding for designing bidirectional cathodes and gives a route for developing reversible AAMCBs and similar metal–gas battery systems.

Thermal stability of MDM and oxidative decomposition mechanism under the condition of air infiltration: A combined experimental, ReaxFF-MD and DFT study

When siloxanes are used as the working fluids of organic Rankine cycle systems, there is a possibility of air infiltration due to the large vacuum present in the condenser. To address this issue, experimental and simulation studies were conducted on the thermal stability and pyrolysis product characteristics of octamethyltrisiloxane (MDM) in the presence of air. The results indicated that air significantly promotes the decomposition of MDM at the temperatures above 250 °C. Air was helpful in increasing the formation of CH2O, CO, and CO2, and also enhanced polymerization of decomposition product of siloxane, leading to higher yields of MD2M, MD3M, MD4M, D3, D4, and D5. By combining reactive force field molecular dynamics (ReaxFF-MD) simulations with density functional theory (DFT) calculations, the microscopic mechanisms of MDM's oxidative decomposition by O2 in air and H2O under humid conditions were elucidated. O2 can easily combine with unsaturated Si atoms to form Si-O bonds, combine with CH3 radicals to form C-O bonds, and undergoes hydrogen extraction reactions to initiate decomposition. H2O mainly undergoes initial decomposition through CH3 radical hydrogenation and binding with unsaturated Si atoms. The generated O2H, O, and OH radicals tend to bind with Si atoms in molecules and radicals, facilitating polymerization reactions.

Toward Gaussian Process Regression Modeling of a Urea Force Field.

FFLUX is a next-generation, machine-learnt force field built on three cornerstones: quantum chemical topology, Gaussian process regression, and (high-rank) multipolar electrostatics. It is capable of performing molecular dynamics with near-quantum accuracy at a lower computational cost than standard ab initio molecular dynamics. Previous work with FFLUX was concerned with water and formamide. In this study, we go one step further and challenge FFLUX to model urea, a larger and more flexible system. In result, we have trained urea models at the B3LYP/aug-cc-pVTZ level of theory, with a mean absolute error of 0.4 kJ mol-1 and a maximum prediction error below 7.0 kJ mol-1. To test their performance in molecular dynamics simulations, two sets of FFLUX geometry optimizations were carried out: 5 dimers corresponding to energy minima and 75 random dimers. The 5 dimers were recovered with a root-mean-square deviation below 0.1 Å with respect to their ab initio references. Out of the 75 random dimers, 68% converged to the qualitatively same dimer as those obtained at the ab initio level. Furthermore, we have ranked the 5 FFLUX-optimized dimers in the order of their relative FFLUX single-point energies and compared them with the ab initio method. The energy ranking fully agreed but for one crossover between two successive minima. Finally, we have demonstrated the importance of geometry-dependent (i.e., flexible) multipole moments, showing that the lack of multipole moment flexibility can lead to average errors in the total intermolecular electrostatic energy of more than 2 orders of magnitude.

Exploring the structural feature of water, alcohols, and their binary mixtures with concrete atomic charge assignments in Dreiding forcefield

The water, alcohols, and their binary mixtures are widely used in molecular simulations. However, the Dreiding force field lacks a generally accepted method for assigning atomic charges to these solvents during simulations. In this study, we propose a universal charge assignment for water and eight water-miscible alcohols in Dreiding. Through extensive molecular simulations, we demonstrate the good accuracy of our charge assignments in displaying characteristic of these solvents and their mixtures, including liquid density and structure. Moreover, we investigate equilibrium snapshot, radial distribution function, coordination number and hydrogen bonding, all of which confirm the miscibility of alcohols with water or ethanol. Notably, we reveal that the structure diversity among different mixtures can be attributed to distinctive characteristic of alcohols, including molecular volume, as well as the number and position of hydroxyls.

Unveiling Carbon Cluster Coating in Graphene CVD on MgO: Combining Machine Learning Force field and DFT Modeling.

In this study, we investigate the behavior of carbon clusters (Cn, where n ranges from 16 to 26) supported on the surface of MgO. We consider the impact of doping with common impurities (such as Si, Mn, Ca, Fe, and Al) that are typically found in ores. Our approach combines density functional theory calculations with machine learning force field molecular dynamics simulations. It is found that the C21 cluster, featuring a core-shell structure composed of three pentagons isolated by three hexagons, demonstrates exceptional stability on the MgO surface and behaves as an "enhanced binding agent" on MgO-doped surfaces. The molecular dynamics trajectories reveal that the stable C21 coating on the MgO surface exhibits less mobility compared to other sizes Cn clusters and the flexible graphene layer on MgO. Furthermore, this stability persists even at temperatures up to 1100K. The analysis of the electron localization function and potential function of Cn on MgO reveals the high localization electron density between the central carbon of the C21 ring and the MgO surface. This work proposes that the C21 island serves as a superstable and less mobile precursor coating on MgO surfaces. This explanation sheds light on the experimental defects observed in graphene products, which can be attributed to the reduced mobility of carbon islands on a substrate that remains frozen and unchanged.

Absolute Binding Free Energies with OneOPES.

The calculation of absolute binding free energies (ABFEs) for protein-ligand systems has long been a challenge. Recently, refined force fields and algorithms have improved the quality of the ABFE calculations. However, achieving the level of accuracy required to inform drug discovery efforts remains difficult. Here, we present a transferable enhanced sampling strategy to accurately calculate absolute binding free energies using OneOPES with simple geometric collective variables. We tested the strategy on two protein targets, BRD4 and Hsp90, complexed with a total of 17 chemically diverse ligands, including both molecular fragments and drug-like molecules. Our results show that OneOPES accurately predicts protein-ligand binding affinities with a mean unsigned error within 1 kcal mol-1 of experimentally determined free energies, without the need to tailor the collective variables to each system. Furthermore, our strategy effectively samples different ligand binding modes and consistently matches the experimentally determined structures regardless of the initial protein-ligand configuration. Our results suggest that the proposed OneOPES strategy can be used to inform lead optimization campaigns in drug discovery and to study protein-ligand binding and unbinding mechanisms.

Kinetic insight of multi-molecule adsorption behaviors into the anticorrosion performance of azole inhibitors: A reactive force field molecular dynamics study

Density functional theory (DFT) calculations and molecular dynamics (MD) simulations are established as powerful tools in understanding the anti-corrosion mechanism of inhibitors at the atomic scale. Despite their widespread popularity, DFT calculations typically focus on small adsorption models while the inhibition efficiency of inhibitors is determined by multi-molecule adsorption film on metal surface. Classical MD simulations neglect the formation of chemical bonding, which frequently governs the overall adsorption process. In this contribution, reactive force field (ReaxFF) MD simulations were employed to overcome these limitations and investigate the differences in inhibitory capabilities among three classic azole compounds: imidazole (IDZ), 1,2,4-triazole (TAZ), and benzotriazole (BTA) on copper. Electrochemical tests and microscopic observations revealed that BTA demonstrated superior inhibitory effect on copper corrosion compared to IDZ and TAZ. However, the widely used descriptor, adsorption energy (Eads) in DFT calculations for predicting the inhibition ability of inhibitors were very similar, failing to explain the enormous variation in inhibition effectiveness. To explain this disparity, multi-molecule adsorption kinetics based on ReaxFF simulations were conducted. The results showed that BTA consistently demonstrated faster and more frequent adsorption on copper surface compared to the other two. The significant variance in inhibitory efficiency among the three inhibitors was elucidated for the first time from the kinetic insight.

Predicting the properties of bitumen using machine learning models trained with force field atom types and molecular dynamics simulations

This study enhances the molecular analysis of bitumen by transitioning from traditional chemical descriptors, such as SARA (Saturates, Aromatics, Resins, and Asphaltenes) fractions and elemental compositions, to specific force field atom types in Molecular Dynamics (MD) models. This shift improves the precision in predicting material properties critical for bituminous material characterization. Machine Learning Models (MLMs) were developed to use these atom types as input features, inherently reflecting fundamental chemical characteristics. Trained on data from over 1,770 LAMMPS simulations of diverse bitumen types and conditions, these MLMs enable the prediction of properties like density, heat capacity, solubility parameters, and thermal expansion coefficients without the need for additional MD simulations. The models utilize 30 chemical descriptors corresponding to specific atom types in the PCFF force field, which collectively account for over 95% of the influence on these properties. By accurately predicting fundamental, thermodynamic, and kinetic properties, the use of MLMs and force field atom types allows researchers to efficiently tweak the chemical nature of organic molecules and mixtures to achieve desired properties. With near-instantaneous prediction times, these MLMs offer valuable insights for advancing bitumen research in the construction and petroleum industries, reducing the need for more intensive simulation techniques.