Force Field Research Articles

Conversion of preferred crystalline orientation by annealing and its impacts on the structural, electronic, and optical properties of pulsed laser-deposited CdO thin films

The impact of annealing on the preferred crystalline orientation as well as the structural, electronic, and optical properties of pulsed laser-deposited 150 ± 3 nm thick cadmium oxide (CdO) films was studied. X-ray diffraction analysis revealed that the annealed CdO films have smaller crystallite sizes, as their molecules at the grain boundaries possessed enough kinetic energy to rearrange their preferred crystalline orientation between (111) and (200). The atomic force microscopy and field emission scanning electron microscopy analyses revealed an enhancement in the active surface area with optimized grain boundaries by annealing which facilitates gas adsorption/desorption. DC electrical conductivity depends on the annealing process and reveals two different conduction mechanisms. The direct optical energy gap was decreased from 3.41 eV to 2.76 eV by annealing at 473 K which is greater than the theoretical value, i.e. 2.2 eV, for bulk CdO. The findings agree with the Burstein-Moss effect as a result of the increased carrier concentration following the generation of numerous oxygen vacancy sites and Cd interstitials. However, mutual charge carrier-impurity interaction and charge carrier Columb interaction contributed to narrowing the band gap by increasing the annealing temperature. The enhanced values of Urbach energy, optical packing density, dispersion parameters, optical conductivity, dissipation factor, oscillator strength, and lattice dielectric constant supported the effectiveness of the annealing process on tailoring optoelectronic responses in the infrared region for the CdO thin films for use in optoelectronic devices. Moreover, the calculated linear optical susceptibility, and nonlinear constants (i.e. third-order optical susceptibility and nonlinear refractive index) were the highest for CdO films annealed at 473 K. The improved value of the figure of merit, solar material protection factor, and solar skin protection factor for the CdO films, annealed at 473 K supports its use in several photovoltaic applications, including solar cells.

Influence essential of current on electrochemical plasticization of single-crystal copper

Despite the long-standing identification of electrochemical plasticization (EP) in the metal surface layer, the mechanisms governing the impact of electric current density on EP remain incompletely understood. This study investigates the effect of current density (ranging from 0 mA cm−2 to 10.0 mA cm−2) on EP during electrochemical cold drawing of single-crystal copper in a 0.35 mol L−1 dilute H2SO4 electrolyte through microstructural characterizations and reactive force field molecular dynamic simulations. The results indicate that with increasing density, more atoms in the high-energy {110} and {001} planes in the copper surface are selectively dissolved, causing the atoms in the low-energy uncorroded {111} planes to become more loosened. This activates more abundant dislocations in a multi-slip way, thereby facilitating dislocation entanglements to reconfigure into movable short-range wavy dislocations and decreasing dislocation density through reactions, ultimately resulting in improved EP. However, when the density exceeds 6.7 mA cm–2, an opposite effect is observed due to the dissolution of fewer atoms resulting from enhanced local passivation.

Study of Ammonia Adsorption on Magnetite Surfaces with Molecular Dynamics Simulations.

The present study proposes an atomistic molecular dynamics model system of a magnetite (Fe3O4) {111} surface. The effect of temperature on the adsorption process of ammonia (NH3) at low concentrations in the aqueous phase has been considered. The molecular dynamics simulations were carried out using the Clay force field (Clay FF) with a modification for the iron atoms in the NPT ensemble at a pressure of 90 bar. The considered system was heated in a temperature range from 293 to 473 K, and additional relaxations were performed at temperatures of interest. Within the scope of this study, the basic parameters of the magnetite surface were calculated and the distances between the ammonia molecules and the surface were determined. A general idea of the degree and rate of adsorption at specific temperatures was obtained. The calculation results were compared to the experimental data where possible and to other available simulations of adsorption processes on metal oxides.

Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces.

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and complex interactions. A recent class of advanced MLIPs, which use equivariant representations and deep graph neural networks, is known as universal models. These models are proposed as foundation models suitable for any system, covering most elements from the periodic table. Current universal MLIPs (UIPs) have been trained with the largest consistent data set available nowadays. However, these are composed mostly of bulk materials' DFT calculations. In this article, we assess the universality of all openly available UIPs, namely MACE, CHGNet, and M3GNet, in a representative task of generalization: calculation of surface energies. We find that the out-of-the-box foundation models have significant shortcomings in this task, with errors correlated to the total energy of surface simulations, having an out-of-domain distance from the training data set. Our results show that while UIPs are an efficient starting point for fine-tuning specialized models, we envision the potential of increasing the coverage of the materials space toward universal training data sets for MLIPs.

The Conformation of Glycosidic Linkages According to Various Force Fields: Monte Carlo Modeling of Polysaccharides Based on Extrapolation of Short-Chain Properties.

The conformational features of the glycosidic linkage are the most important variable to consider when studying di-, oligo-, and polysaccharide molecules using molecular dynamics (MD) simulations. The accuracy of the theoretical model describing this degree of freedom influences the quality of the results obtained from MD calculations based on this model. This article focuses on the following two issues related to the conformation of the glycosidic linkage. First, we describe the results of a comparative analysis of the predictions of three carbohydrate-dedicated classical force fields for MD simulations, namely, CHARMM, GLYCAM, and GROMOS, in the context of different parameters of structural and energetic nature related to the conformation of selected types of glycosidic linkages, α(1 → 4), β(1 → 3), and β(1 → 4), connecting glucopyranose units. This analysis revealed several differences, mainly concerning the energy levels of the secondary and tertiary conformers and the linkage flexibility within the dominant exo-syn conformation for α(1 → 4) and β(1 → 3) linkages. Some aspects of the comparative analysis also included the newly developed, carbohydrate-dedicated Martini 3 coarse-grained force field. Second, to overcome the time-scale problem associated with sampling slow degrees of freedom in polysaccharide chains during MD simulations, we developed a coarse-grained (CG) model based on the data from MD simulations and designed for Monte Carlo modeling. This model (CG MC) is based on information from simulations of short saccharide chains, effectively sampled in atomistic MD simulations, and is capable of extrapolating local conformational properties to the case of polysaccharides of arbitrary length. The CG MC model has the potential to estimate the conformations of very long polysaccharide chains, taking into account the influence of secondary and tertiary conformations of glycosidic linkages. With respect to the comparative analysis of force fields, the application of CG MC modeling showed that relatively small differences in the predictions of individual force fields with respect to a single glycosidic linkage accumulate when considering their effect on the structure of longer chains, leading to drastically different predictions with respect to parameters describing the polymer conformation, such as the persistence length.

In silico-guided discovery and in vitro validation of novel sugar-tethered lysinated carbon nanotubes for targeted drug delivery of doxorubicin

ContextMultiwalled carbon nanotubes (MWCNTs) functionalized with lysine via 1,3-dipolar cycloaddition and conjugated to galactose or mannose are potential nanocarriers that can effectively bind to the lectin receptor in MDA-MB-231 or MCF-7 breast cancer cells. In this work, a method based on molecular dynamics (MD) simulation was used to predict the interaction of these functionalized MWCNTs with doxorubicin and obtain structural evidence that allows a better understanding of the drug loading and release process. The MD simulations showed that while doxorubicin only interacted with pristine MWCNTs through π-π stacking interactions, functionalized MWCNTs were also able to establish hydrogen bonds, suggesting that the functionalized groups improve doxorubicin loading. Moreover, the elevated adsorption levels observed for functionalized nanotubes further support this enhancement in loading efficiency. MD simulations also shed light on the intratumoral pH-specific release of doxorubicin from functionalized MWCNTs, which is induced by protonation of the daunosamine moiety. The simulations show that this change in protonation leads to a lower absorption of doxorubicin to the MWCNTs. The MD studies were then experimentally validated, where functionalized MWCNTs showed improved dispersion in aqueous medium compared to pristine MWCNTs and, in agreement with the computational predictions, increased drug loading capacity. Doxorubicin-loaded functionalized MWCNTs demonstrated specific release of doxorubicin in tumor microenvironment (pH = 5.0) with negligible release in the physiological pH (pH = 7.4). Furthermore, doxorubicin-free MWNCT nanoformulations exhibited insignificant cytotoxicity. The experimental studies yielded nearly identical results to the MD studies, underlining the usefulness of the method. Our functionalized MWCNTs represent promising non-toxic nanoplatforms with enhanced aqueous dispersibility and the potential for conjugation with ligands for targeted delivery of anti-cancer drugs to breast cancer cells.MethodsThe computational model of a pristine carbon nanotube was created with the buildCstruct 1.2 Python script. The lysinated functionalized groups were added with PyMOL and VMD. The carbon nanotubes and doxorubicin molecules were parameterized using the general AMBER force field, and RESP charges were determined using Gaussian 09. Molecular dynamics simulations were carried out with the AMBER 20 software package. Adsorption levels were calculated using the water-shell function of cpptraj. Cytotoxicity was evaluated via a MTT assay using MDA-MB-231 and MCF-7 breast cancer cells. Drug uptake of doxorubicin and doxorubicin-loaded MWCNTs was measured by fluorescence microscopy.

Simulation of Hydrogen Adsorption in Hierarchical Silicalite: Role of Electrostatics and Surface Chemistry.

Adsorption in nanoporous materials is one strategy that can be used to store hydrogen at conditions of temperature and pressure that are economically viable. Adsorption capacity of nanoporous materials depends on surface area which can be enhanced by incorporating a hierarchical pore structure. We report grand canonical Monte Carlo (GCMC) simulation results on the adsorption of hydrogen in hierarchical models of silicalite that incorporate 4 nm wide mesopores in addition to the 0.5 nm wide micropores at 298 K, using different force fields to model hydrogen. Our results suggest that incorporating mesopores in silicalite can enhance adsorption by at least 20% if electrostatic interactions are not included and up to 100% otherwise. Incorporating electrostatic interactions results in higher adsorption by close to 100% at lower pressures for hierarchical silicalite whereas for unmodified silicalite, it is less significant at all pressures. Hydroxylating the mesopore surface in hierarchical silicalite results in an enhancement in adsorption at pressures below 1 atm and suppression by up to 20% at higher pressures. Temperature dependence at selected pressures exhibits expected decrease in adsorption amounts at higher temperatures. These findings can be useful in the engineering, selection, and optimization of nanoporous materials for hydrogen storage.

Excited-Ground-State Transition of the RNA Strand Slippage Mechanism Captured by the Base-Specific Force Field.

Excited-ground-state transition and strand slippage of RNA play key roles in transcription and translation of central dogma. Due to limitation of current experimental techniques, the dynamic structure ensembles of RNA remain inadequately understood. Molecular dynamics simulations offer a promising complementary approach, whose accuracy depends on the force field. Here, we develop the new version of RNA base-specific force field (BSFF2) to address underestimation of base pairing stability and artificial backbone conformations. Extensive evaluations on typical RNA systems have comprehensively confirmed the accuracy of BSFF2. Furthermore, BSFF2 demonstrates exceptional efficiency in de novo folding of tetraloops and reproducing base pair reshuffling transition between RNA excited and ground states. Then, we explored the RNA strand slippage mechanism with BSFF2. We conducted a comprehensive three-dimensional structural investigation into the strand slippage of the most complex r(G4C2)9 repeat element and presented the molecular details in the dynamic transition along with the underlying mechanism. Our results of capturing the strand slippage, excited-ground transition, de novo folding, and simulations for various typical RNA motifs indicate that BSFF2 should be one of valuable tools for dynamic conformation research and structure prediction of RNA, and a future contribution to RNA-targeted drug design as well as RNA therapy development.

Incorporating Noncovalent Interactions in Transfer Learning Gaussian Process Regression Models for Molecular Simulations.

FFLUX is a quantum chemical topology-based multipolar force field that uses Gaussian process regression machine learning models to predict atomic energies and multipole moments on the fly for fast and accurate molecular dynamics simulations. These models have previously been trained on monomers, meaning that many-body effects, for example, intermolecular charge transfer, are missed in simulations. Moreover, dispersion and repulsion have been modeled using Lennard-Jones potentials, necessitating careful parametrization. In this work, we take an important step toward addressing these shortcomings and show that models trained on clusters, in this case, a dimer, can be used in FFLUX simulations by preparing and benchmarking a formamide dimer model. To mitigate the computational costs associated with training higher-dimensional models, we rely on the transfer of hyperparameters from a smaller source model to a larger target model, enabling an order of magnitude faster training than with a direct learning approach. The dimer model allows for simulations that account for two-body effects, including intermolecular polarization and charge penetration, and that do not require nonbonded potentials. We show that addressing these limitations allows for simulations that are closer to quantum mechanics than previously possible with the monomeric models.

Understanding the Role of 1/2 ⟨110⟩ Dislocations in Deformation Mechanisms of Single-Crystal High-Entropy Carbide Ceramics from Machine Learning Force Field Simulations

Understanding the Role of 1/2 ⟨110⟩ Dislocations in Deformation Mechanisms of Single-Crystal High-Entropy Carbide Ceramics from Machine Learning Force Field Simulations

Coarse-Grained Modeling of Liquid-Liquid Phase Separation in Cells: Challenges and Opportunities.

Liquid-liquid phase separation (LLPS) within cells gives rise to membraneless organelles, which play pivotal roles in numerous cellular functions. A comprehensive understanding of the functional aspects of intrinsically disordered protein (IDP) condensates necessitates elucidating their inherent structures and establishing correlations with biological functions. Coarse-grained (CG) molecular dynamics (MD) simulations present a promising avenue for gaining insights into LLPS mechanisms of biomacromolecules. Essential to this endeavor is the development of tailored CG force fields for MD simulations, incorporating the full spectrum of biomolecules involved in the formation of condensates and accounting for real-time biochemical reactions coupled to the LLPS. Moreover, developing accurate theoretical frameworks and establishing links between condensate structure and its function are imperative for a thorough comprehension of LLPS of biological systems.

Study of the antidiabetic mechanism of berberine compound on FOXO1 transcription factor through molecular docking and molecular dynamics simulations.

Diabetes mellitus (DM) is a metabolic disorder disease that causes hyperglycemia conditions and associated with various chronic complications leading to mortality. Due to high toxicity of conventional diabetic drugs, the exploration of natural compounds as alternative diabetes treatments has been widely carried out. Previous in silico studies have highlighted berberine, a natural compound, as a promising alternative in antidiabetic therapy, potentially acting through various pathways, including the inhibition of the FOXO1 transcription factor in the gluconeogenesis pathway. However, the specific mechanism by which berberine interacts with FOXO1 remains unclear, and research in this area is relatively limited. Therefore, this study aims to determine the stability of berberine structure with FOXO1 based on RMSD, RMSF, binding energy, and trajectory analysis to determine the potential of berberine to inhibit the gluconeogenesis pathway. This research was conducted by in silico method with molecular docking using AutoDock4.2 and molecular dynamics study using Amber20, then visualized by VMD. Docking between ligand and FOXO1 receptor was carried out with Autodock4.2. For molecular dynamics simulations, the force fields of DNA.OL15, protein.ff14SB, gaff2, and tip3p were used.

Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.

We present a comprehensive study investigating the potential gain in accuracy for calculating absolute solvation free energies (ASFE) using a neural network potential to describe the intramolecular energy of the solute. We calculated the ASFE for most compounds from the FreeSolv database using the Open Force Field (OpenFF) and compared them to earlier results obtained with the CHARMM General Force Field (CGenFF). By applying a nonequilibrium (NEQ) switching approach between the molecular mechanics (MM) description (either OpenFF or CGenFF) and the neural net potential (NNP)/MM level of theory (using ANI-2x as the NNP potential), we attempted to improve the accuracy of the calculated ASFEs. The predictive performance of the results did not change when this approach was applied to all 589 small molecules in the FreeSolv database that ANI-2x can describe. When selecting a subset of 156 molecules, focusing on compounds where the force fields performed poorly, we saw a slight improvement in the root-mean-square error (RMSE) and mean absolute error (MAE). The majority of our calculations utilized unidirectional NEQ protocols based on Jarzynski's equation. Additionally, we conducted bidirectional NEQ switching for a subset of 156 solutes. Notably, only a small fraction (10 out of 156) exhibited statistically significant discrepancies between unidirectional and bidirectional NEQ switching free energy estimates.

Thermomechanical Properties of Transition Metal Dichalcogenides Predicted by a Machine Learning Parameterized Force Field.

The mechanical and thermal properties of transition metal dichalcogenides (TMDs) are directly relevant to their applications in electronics, thermoelectric devices, and heat management systems. In this study, we use a machine learning (ML) approach to parametrize molecular dynamics (MD) force fields to predict the mechanical and thermal transport properties of a library of monolayered TMDs (MoS2, MoTe2, WSe2, WS2, and ReS2). The ML-trained force fields were then employed in equilibrium MD simulations to calculate the lattice thermal conductivities of the foregoing TMDs and to investigate how they are affected by small and large mechanical strains. Furthermore, using nonequilibrium MD, we studied thermal transport across grain boundaries. The presented approach provides a fast albeit accurate methodology to compute both mechanical and thermal properties of TMDs, especially for relatively large systems and spatially complex structures, where density functional theory computational cost is prohibitive.

Molecular Dynamics Simulations of Soft and Reactive Landing of Proteins Desorbed by Argon Cluster Bombardment.

Reactive molecular dynamics (MD) simulations were conducted to investigate the soft and reactive landing of hyperthermal velocity proteins transferred to a vacuum using large argon clusters. Experimentally, the interaction of argon cluster ion beams (Ar1000-5000+) with a target biofilm was previously used in such a manner to transfer lysozymes onto a collector with the retention of their bioactivity, paving the way to a new solvent-free method for complex biosurface nanofabrication. However, the experiments did not give access to a microscopic view of the interactions needed for their full understanding, which can be provided by the MD model. Our reactive force field simulations clarify the landing mechanisms of the lysozymes and their fragments on collectors with different natures (gold- and hydrogen-terminated graphite). The results highlight the conditions of soft and reactive landing on rigid surfaces, the effects of the protein structure, energy, and incidence angle before landing, and the adhesion forces with the collector substrate. Many of the obtained results can be generalized to other soft and reactive landing approaches used for biomolecules such as electrospray ionization and matrix-assisted laser desorption ionization.

Determining interfacial tension and critical micelle concentrations of surfactants from atomistic molecular simulations

HypothesisAtomistically-detailed models of surfactants provide quantitative information on the molecular interactions and spatial distributions at fluid interfaces. Hence, it should be possible to extract from this information, macroscopical thermophysical properties such as interfacial tension, critical micelle concentrations and the relationship between these properties and the bulk fluid surfactant concentrations.Simulations and ExperimentsMolecular-scale interfacial of systems containing n-dodecyl β-glucoside (APG12) are simulated using classical molecular dynamics. The bulk phases and the corresponding interfacial regions are all explicitly detailed using an all-atom force field (PCFF+). During the simulation, the behaviour of the interface is analyzed geometrically to obtain an approximated value of the critical micelle concentration (CMC) in terms of the surfactant area number density and the interfacial tension is assessed through the analysis of the forces amongst molecules.New experimental determinations are reported for the surface tension of APG12 at the water/air and at the water/n-decane interfaces.FindingsWe showcase the application of a thermodynamic framework that inter-relates interfacial tensions, surface densities, CMCs and bulk surfactant concentrations, which allows the in silico quantitative prediction of interfacial tension isotherms.

Overcoming the Chemical Complexity Bottleneck in on-the-Fly Machine Learned Molecular Dynamics Simulations.

We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 elements, we demonstrate that the number of density functional theory calls remains approximately independent of the number of chemical elements, in contrast to the increase in the smooth overlap of the atomic positions scheme.

Amesp: Atomic and molecular electronic structure program

The atomic and molecular electronic structure program (Amesp) is a general-purpose electronic structure program designed for the study of molecular electronic structures. It incorporates a series of modern electronic structure methods, including Hartree–Fock, density functional theory, Multiconfigurational self-consistent field, Møller–Plesset, configuration-interaction, coupled-cluster, semiempirical methods, and molecular force fields. Amesp strives to offer an efficient and user-friendly tool specifically designed for computing for molecules ranging from small to complex biomolecules. In this paper, we highlight the features of Amesp and offer an overview.

Temperature dependence of the friction and wear behavior of pure tungsten

In this work, the friction and wear characteristics of rolled pure tungsten were investigated by using atomic force microscope (AFM) and field emission scanning electron microscope (SEM) with electron backscatter diffraction (EBSD) characterization techniques. A ductile to brittle transition (DBT) related frictional wear behaviors have been discovered. The results indicated that the coefficient of friction maintains an almost constant value of 0.45 at low temperatures (≤ 400 °C), whereas it consistently decreases at 600 °C due to the generation of a surface oxide. The wear rate varies with temperatures and reaches a maximum at 200 °C due to the transition of the tungsten matrix from brittleness to pseudoplasticity, which leads to severe adhesive wear and large mass loss. In addition, the evolution of sub-grains underneath the friction layer and Vickers microhardness were evaluated as well. This work provides insight into understanding the relationship between temperatures, microstructure and the frictional properties of tungsten materials, and thus provides guidance for improving frictional wear behaviors in tungsten materials.

Molecular dynamics simulation on crystal morphology of NH4ClO4.

Ammonium perchlorate (NH4ClO4, abbreviated as AP) has the advantages of high oxygen content, high density, and good compatibility, and has significant application prospects in the field of energetic materials. The crystal morphology has a great influence on the properties and sensibility of energetic materials, and a single experimental means is difficult in exploring the crystals; therefore, the crystal morphology of AP is investigated using molecular dynamics simulation complemented with experiments, to theoretically analyze the differences in AP crystal habit and the interactions between solvent molecules and the main growing crystal surfaces of AP. The results show that AP crystal is mainly composed of five independent crystal surfaces (0 0 1), (0 1 0), (1 0 0), (1 0 1), and (1 0 -1) in vacuum using the BFDH laws, with (0 0 1) surface being the main growth crystal surface. In contrast, in H2O solvent, the (1 0 1) and (1 0 -1) surfaces disappear, and the AP mainly consists of (0 0 1), (0 1 0), and (1 0 0) surfaces with a rectangular shape. The crystal morphology obtained from theoretical prediction is in good agreement with that obtained from experimental culture. This paper can provide a new idea for the cultivation and preparation of AP large crystals, and promote the application of AP crystals in energetic materials. The crystal morphologies of AP in vacuum and H2O solvent under Dreiding force field were predicted based on attachment energy model by using molecular dynamics method in Materials Studio 2019 software. The entire molecular dynamics simulation was carried out under the NTV system, the temperature control method was selected as Anderson, and the system temperature was set to 298 K. The simulation time was set to 40 ps, the step size was set to 1 fs, and the data were outputted every 5000 steps.