Dynamics Simulations Of Molecules Research Articles

Density functional theory and molecular dynamics simulation of water molecules confined between parallel graphene oxide surfaces using new interaction potentials

In this study, the interaction potential of water molecule with a graphene oxide (GO) plate containing OH and O groups has been calculated using the M06-2X/6-31g(d,p) level of theory at different orientations and intermolecular distances and fitted to the Born-Huggins-Meyer (BHM) model. There are good agreements between the calculated and the OPLS-AA and Dreiding models, especially for the GO(O)-H2O interactions. To examine the new computed models, we have used the closer potentials to the OPLS-AA and Dreiding models in the molecular dynamics (MD) simulations. We have calculated several properties using the different obtained interaction potentials including average number of hydrogen bonds per water molecule (〈HB〉) between confined water molecules and between water and GO-surfaces, radial distribution function (RDF), self-diffusion coefficient, and angle distribution function of the confined water molecules between the GO plates. Our results showed good agreements between the OPLS-AA and Dreiding models and some calculated potentials. However, some calculated models showed completely different behavior which discussed in details. According to the results, we concluded that the OH-2 and O1-OH2 models show totally better agreement with the famous force fields than the other calculated potentials. This work provides a simple method for the development of new force fields specifically for these types of systems which are in good agreement with the well-known force fields.

Maintaining the concentration equilibrium of proton and hydroxide ions by construction of bis(2-aminoethyl)amine-based inner helmholtz plane toward long-life zinc metal anodes

Despite the merits of high safety and low cost, the development of rechargeable aqueous Zn metal batteries is oppressed by the short cycling life of Zn metal anodes, due to the hydrogen evolution reaction (HER) and the formation of irreversible Zn4SO4(OH)6·nH2O that are caused by the decomposition of H2O molecules adsorbed in the inner Helmholtz plane (IHP) of Zn metal anodes. To solve this issue, we herein use bis(2-aminoethyl)amine (B2AA) organic molecules as additives to suppress Zn corrosion and HER via reconstructing the IHP and maintaining the concentration equilibrium of protons and hydroxide ions, thus achieving long-term and highly reversible Zn metal batteries. Density functional theory calculations and molecule dynamics simulations reveal that B2AA are preferentially adsorbed on the interface of Zn anode owing to the high adsorption energy, forming a water-deficient IHP via repelling ≈19% of H2O molecules. Additionally, the −NH2/−NH− groups of B2AA can reversibly capture H+ derived from the decomposition of H2O and dynamically neutralize OH−, thus suppressing the corrosion reaction. Using an aqueous electrolyte involving B2AA, symmetric Zn//Zn cells exhibit a long cycling life of 2400 h at 10 mA cm−2. Matched with a NH4V4O10 (NVO) cathode, the Zn//NVO full cell delivers a high initial capacity of 147.24 mAh/g at 10 A/g, and maintains a high capacity retention of 66% after 3000 cycles.

Crucial gating residues govern the enhancement of peroxygenase activity in an engineered cytochrome P450 O-demethylase.

P450-catalyzed O-demethylation reactions have recently attracted particular attention because of their potential applications in lignin bioconversion. We recently enabled the peroxygenase activity of CYP199A4, a NADH-dependent cytochrome P450 monooxygenase from Rhodopseudomonas palustris, by engineering a hydrogen peroxide (H2O2) tunnel. In this report, we reveal by crystallography and molecule dynamics simulations that key residues located at one of the water tunnels in CYP199A4 play a crucial gating role, which enhances the peroxygenase activity by regulating the inflow of H2O2. These results provide a more complete understanding of the mechanism by which monooxygenase is converted into peroxygenase activity through the H2O2 tunnel engineering (HTE) strategy. Furthermore, a library of engineered CYP199A4 peroxygenases was constructed to explore their application potentials for O-demethylation of various methoxy-substituted benzoic acid derivatives. The engineered CYP199A4 peroxygenases showed good functional group tolerance and preferential O-demethylation at the meta- or para-position, indicating potential O-demethylation of H- and G-type lignin monomers. This work reveals the feasibility of the HTE strategy in creating P450 peroxygenase from a mechanistic perspective, laying the foundation for developing an effective P450 O-demethylase applicable in lignin bioconversion.

Fluorinated polymer zwitterions on gold nanoparticles: patterned catalyst surfaces guide interfacial transport and electrochemical CO2 reduction.

We report the use of fluorinated polymer zwitterions to build hybrid systems for efficient CO2 electroreduction. The unique combination of hydrophilic phosphorylcholine and hydrophobic fluorinated moieties in these polymers creates a fractal structure with mixed branched cylinders on the surface of gold nanoparticles (AuNPs). In the presence of these polymers, the CO faradaic efficiency improves by 50-80% in the range of -0.7 V to -0.9 V. The fractal structures have a domain size of ∼3 nm, showing enhanced mass transfer kinetics of CO2 approaching the catalyst surfaces without limiting ion diffusion. The phase-separated hydrophilic and hydrophobic domains offer separated channeling to water and CO2, as confirmed by attenuated total reflectance surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) and molecule dynamic (MD) simulations. H2O molecules permeate extensively into the polymer layer that adsorbs on zwitterions, forming continuous chains, while CO2 molecules strongly associate with the fluorinated tails of fluorinated polyzwitterions, with oxygen facing the positively charged amine groups. Overall, this coupling of zwitterion and fluorocarbon in a polymer material creates new opportunities for defining microenvironments of metallic nanocatalysts in hybrid structures.

Pd doped Janus HfSeS monolayer: Ultrahigh sensitive gas sensing material for reversible detection of NO

Recent studies on two-dimensional (2D) Janus transition metal dichalcogenides (TMDs) show that these materials have excellent electronic property and raised very promising prospects for applying them as gas sensor materials. Here we consider the potential application of replacement doping with Pd on the monolayer HfSeS as well as its pristine structure as gas sensor materials for detecting CO, CO2, NH3 and NO through first principles calculations. The calculated results of Ab initio molecule dynamics (AIMD) simulations as well as phonon spectrum which shows no negative frequencies confirmed the good thermodynamical stability of pristine and doped HfSeS. The formation energies of the doped structures with S and Se atoms substituted by Pd atom are − 5.174 and − 5.168 eV which further proves the stability of the structures. The pristine HfSeS cannot well distinguish these four gas molecules due to very close adsorption energies (range from −0.142 eV to −0.477 eV). However, after the introduction of Pd, the doped structure could effectively achieve the distinguish of these four gas molecules (with adsorption energy ranges from −0.229 eV to −0.933 eV). Furthermore, the recovery time of these gases is within 500 s, which shows that the adsorption is reversible both in pristine and Pd-doped monolayer HfSeS. Most interestingly, Pd-doped monolayer HfSeS exhibits ultrahigh sensitive to NO, as the systems become metallic after NO adsorption. Due to the change in conductivity, enhanced adsorption energy, and together with a relatively lower recovery time, Pd-doped monolayer HfSeS exhibits promising application as gas sensors for reversible detection of NO.

Multi-scale molecular dynamics simulations of enhanced energy transfer in organic molecules under strong coupling

Exciton transport can be enhanced in the strong coupling regime where excitons hybridize with confined light modes to form polaritons. Because polaritons have group velocity, their propagation should be ballistic and long-ranged. However, experiments indicate that organic polaritons propagate in a diffusive manner and more slowly than their group velocity. Here, we resolve this controversy by means of molecular dynamics simulations of Rhodamine molecules in a Fabry-Pérot cavity. Our results suggest that polariton propagation is limited by the cavity lifetime and appears diffusive due to reversible population transfers between polaritonic states that propagate ballistically at their group velocity, and dark states that are stationary. Furthermore, because long-lived dark states transiently trap the excitation, propagation is observed on timescales beyond the intrinsic polariton lifetime. These insights not only help to better understand and interpret experimental observations, but also pave the way towards rational design of molecule-cavity systems for coherent exciton transport.

Novel method for protecting copper: An in-situ click-assembly film on copper surface

The in-situ self-assembled film on the copper surface is prepared via a novel click chemistry reaction between furfuryl mercaptan (FM) and tert-butyl acetylene (TBA). The formation of the click-assembled film on the copper surface is confirmed by Fourier transform infrared spectrum, Raman, and X-ray photoelectron spectroscopy analyses. The corrosion inhibition ability of the click-assembled film is evaluated using electrochemical methods. The impedance results indicate that the click-assembled film exhibits a maximum inhibition efficiency of up to 99.86 %, which is attributed to the formation of the C-S bond. This bond facilitates the attachment of corrosion inhibitors to the copper surface. Quantum chemistry calculations and molecule dynamics simulations are conducted to further elucidate the corrosion inhibition mechanism.

Theoretical design of novel two-dimensional Mo2S2X2 (X=C,Si) and their electronic and mechanical properties

Novel two-dimensional Mo2S2C2 and Mo2S2Si2 are theoretically designed and their stability, electronic and mechanical properties are investigated by using first principles calculations. The absent of imaginary frequencies in phonon dispersion and no atomic bond broken in molecule dynamic simulation well confirm the dynamic and thermal stability of Mo2S2C2 and Mo2S2Si2. Further calculations indicate both Mo2S2C2 and Mo2S2Si2 possess excellent mechanical flexibility, which can sustain the critical strains ∼22% and 23%, respectively. The calculated electronic band structures based on HSE06 method reveal that Mo2S2C2 and Mo2S2Si2 are zero band gap materials. In addition, they can form p-type contact Mo2S2C2/MoS2 and Mo2S2Si2/MoS2 heterostructures with the schottky barrier height ∼0.72 eV and 0.62 eV, respectively, which verify they may be potential electrodes for MoS2 based devices.

Molecule Dynamics Simulation of the Effects of Temperature and Vacancy Defects on the Eutectic Sensitivity of CL-20_TNT

For contemporary applications, CL-20’s energy density is the highest of any explosive, but it has a very high susceptibility and very low stability. Eutectic techniques can successfully lower CL-20’s susceptibility. However, the eutectic susceptibility can be significantly impacted by the high temperature and many flaws found in its manufacture. By removing CL-20 molecules in Materials Studio (MS) using a molecular dynamics approach, a CL-20/TNT eutectic model with various vacancy concentrations was created in this study. Equilibrium state simulations of the model were run at various temperatures to determine the corresponding kinetic parameters. The susceptibility of CL-20/TNT eutectic-containing vacancy defects is found to increase with the increase in defect concentration and with the increase in temperature, which guides the study of CL-20’s stability and the reduction of susceptibility methods in engineering preparation. Cohesive energy density and binding energy are used at the molecular level as the evaluation criteria of the susceptibility at the macroscopic level.

Efficient interatomic descriptors for accurate machine learning force fields of extended molecules

Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF. We found that non-local features (atoms separated by as far as 15 Å in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5 Å). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size.

The ACPYPE web server for small-molecule MD topology generation.

The generation of parameter files for molecular dynamics (MD) simulations of small molecules that are suitable for force fields commonly applied to proteins and nucleic acids is often challenging. The ACPYPE software and website aid the generation of such parameter files. ACPYPE uses OpenBabel and ANTECHAMBER to generate MD input files in Gromacs, AMBER, CHARMM, and CNS formats. It can now take a SMILES string as input, in addition to the original PDB or mol2 coordinate files, with GAFF2 support and GLYCAM force field conversion added. It can be installed locally via Anaconda, PyPI, and Docker distributions, while the web server at https://bio2byte.be/acpype/ was updated with an API, and provides visualization of results for uploaded molecules as well as a pre-generated set of 3738 drug molecules. The web application is freely available at https://www.bio2byte.be/acpype/ and the open-source code can be found at https://github.com/alanwilter/acpype.

Computational study on Schiff base derived salicylaldehyde and furfuraldehyde derivatives as potent anti-tubercular agents: prospect to dihydropteroate synthase inhibitors

Nowadays, bacterial multidrug resistance has become a commonplace problem in clinics due to several intrinsic factors mediated through resistance to antibacterials obtained via bacterial consortia and extrinsic factors, such as non-uniform antibacterial policy and migration of resistant bacteria through human and other routes. The development of newer, effective anti-mycobacterial candidate(s) is coveted by clinics. Hybrid molecules would be comparatively more emulating against invasive bacterial strains; nevertheless, newer antibiotics are continually added. Herein, designing and developments of two series of Schiff-based salicylaldehyde S1-S7 and furfuraldehyde F1-F7 molecules individually bearing sulfonamide group are described; and those were synthesized and their structures by spectral characterization were confirmed. Concomitantly, molecule dynamic simulations of all atoms had been performed to fathom the mechanism of the action with these leading complexes. These data imply that the synthesized Schiff-based salicylaldehyde hybrids would be promising anti-tubercular compounds, which further need potent pharmacological evaluations. Communicated by Ramaswamy H. Sarma

Investigating the antiviral therapeutic potentialities of marine polycyclic lamellarin pyrrole alkaloids as promising inhibitors for SARS-CoV-2 and Zika main proteases (Mpro)

The new coronavirus variant (SARS-CoV-2) and Zika virus are two world-wide health pandemics. Along history, natural products-based drugs have always crucially recognized as a main source of valuable medications. Considering the SARS-CoV-2 and Zika main proteases (Mpro) as the re-production key element of the viral cycle and its main target, herein we report an intensive computer-aided virtual screening for a focused list of 39 marine lamellarins pyrrole alkaloids, against SARS-CoV-2 and Zika main proteases (Mpro) using a set of combined modern computational methodologies including molecular docking (MDock), molecule dynamic simulations (MDS) and structure-activity relationships (SARs) as well. Indeed, the molecular docking studies had revealed four promising marine alkaloids including [lamellarin H (14)/K (17)] and [lamellarin S (26)/Z (39)], according to their notable ligand-protein energy scores and relevant binding affinities with the SARS-CoV-2 and Zika (Mpro) pocket residues, respectively. Consequentially, these four chemical hits were further examined thermodynamically though investigating their MD simulations at 100 ns, where they showed prominent stability within the accommodated (Mpro) pockets. Moreover, in-deep SARs studies suggested the crucial roles of the rigid fused polycyclic ring system, particularly aromatic A- and F- rings, position of the phenolic -OH and δ-lactone functionalities as essential structural and pharmacophoric features. Finally, these four promising lamellarins alkaloids were investigated for their in-silico ADME using the SWISS ADME platform, where they displayed appropriated drug-likeness properties. Such motivating outcomes are greatly recommending further in vitro/vivo examinations regarding those lamellarins pyrrole alkaloids (LPAs). Communicated by Ramaswamy H. Sarma

Molecular dynamics simulations of flavour molecules in Scotch whisky

Molecular dynamics simulations are used to investigate the interfacial properties of molecules related to flavours of Scotch whisky, in ethanol/water solutions with different values of alcohol-by-volume (ABV). The propensity of flavour molecules to accumulate at the liquid-vapour interface is linked to the evaporation of those molecules into the head space, and consequently their perception when nosing or tasting. The simulation approach is first validated for ethanol/water solutions, without flavour molecules, by comparing simulated and experimental values of the ethanol surface excess and the interfacial tension. The chosen values of ABV are 0% (pure water), 20% (typical dilution for sensory evaluation), 30% (diluted spirit), 40% (typical bottle strength), 50%, 65% and 73% (representing a range of cask strengths), and 100% (pure ethanol). Then, flavour molecules are considered, ranging from hydrophobic to hydrophilic: octane (alkane); octan-1-ol (alcohol); octanal (aldehyde); octanoic acid (carboxylic acid); and ethyl hexanoate (ester). The primary focus is whether there is a positive excess of such molecules at the interface, or whether the molecules remain fully solvated by the liquid layer. The dependence of this excess on ethanol content can be correlated with how flavour molecules are released into the head space on dilution with water, and hence the tasting experience. Additional molecular-level details are presented, such as how organic molecules are oriented with respect to the interface. This illustrates how molecular simulations could be employed to improve our understanding of the links between composition and flavour perception, or to aid the development of low-alcohol spirits with similar sensory characteristics to full strength equivalents.

Role of ammonia addition on polycyclic aromatic hydrocarbon growth: A ReaxFF molecular dynamics study

Ammonia (NH3), one of the most promising carbon-free fuels, has received great research interest. In particular, NH3 is often blended with hydrocarbon fuels to achieve desired combustion characteristics. However, NH3 addition could affect soot formation, which has not been adequately understood. In this study, the effect of NH3 on the growth of polycyclic aromatic hydrocarbons (PAHs) is investigated with the reactive force field molecule dynamics (ReaxFF MD) simulations and quantum chemistry calculations. The simulation results indicate that NH3 addition slows down the growth of large carbon-containing species in the C2H4/O2 system. Novel path with HCN addition is discovered in the PAH growth, which inhibits the PAH growth compared to the conventional Hydrogen-Abstraction-Carbon-Addition (HACA) path via C2H2 addition. Moreover, quantum chemical calculations verified the rationality of this addition path and explained its inhibition on PAH growth by calculating the heat of reactions and reaction energy barriers. According to the present study, NH3 also has an inhibitory effect on both the HCN addition path and C2H2 addition path by providing H atoms to promote the generation of PAH radicals back to PAH molecules, and this effect is more significant for the C2H2 addition path. Results from the study provide a fundamental insight for the inhibition of PAH growth due to the NH3 addition from an atomistic insight, which helps to improve the understanding of the combustion of blends of NH3 and hydrocarbon fuels.

Data-driven many-body potentials from density functional theory for aqueous phase chemistry

Density functional theory (DFT) has been applied to modeling molecular interactions in water for over three decades. The ubiquity of water in chemical and biological processes demands a unified understanding of its physics, from the single molecule to the thermodynamic limit and everything in between. Recent advances in the development of data-driven and machine-learning potentials have accelerated simulation of water and aqueous systems with DFT accuracy. However, anomalous properties of water in the condensed phase, where a rigorous treatment of both local and non-local many-body (MB) interactions is in order, are often unsatisfactory or partially missing in DFT models of water. In this review, we discuss the modeling of water and aqueous systems based on DFT and provide a comprehensive description of a general theoretical/computational framework for the development of data-driven many-body potentials from DFT reference data. This framework, coined MB-DFT, readily enables efficient many-body molecular dynamics (MD) simulations of small molecules, in both gas and condensed phases, while preserving the accuracy of the underlying DFT model. Theoretical considerations are emphasized, including the role that the delocalization error plays in MB-DFT potentials of water and the possibility to elevate DFT and MB-DFT to near-chemical-accuracy through a density-corrected formalism. The development of the MB-DFT framework is described in detail, along with its application in MB-MD simulations and recent extension to the modeling of reactive processes in solution within a quantum mechanics/MB molecular mechanics (QM/MB-MM) scheme, using water as a prototypical solvent. Finally, we identify open challenges and discuss future directions for MB-DFT and QM/MB-MM simulations in condensed phases.

Size-dependent interfacial thermal transport in supported platinum nanocatalysts

An approach-to-equilibrium molecule dynamics simulation was used to study the heat transfer process and corresponding thermal conductivity of graphene and TiO2-supported platinum (Pt) nanoparticles. The results showed that the larger the number of supported Pt atoms, the faster the particles and support reached the thermal equilibrium. The increased number of supported atoms and the enhanced metal-support interactions (MSIs) led to increased interfacial thermal conductance. According to the analysis of the phonon density of states, the increased interfacial thermal conductance was attributed to the vibrational frequencies of the particles and TiO2 support (strong MSI) being concentrated in the low-frequency region, improving the degree of coupling and promoting heat transfer. In addition, by combining a large amount of simulation data and existing models, we established a simple prediction model for evaluating the interfacial thermal conductance of supported Pt catalysts.

Machine Learning Diffusion Monte Carlo Forces

Diffusion Monte Carlo (DMC) is one of the most accurate techniques available for calculating the electronic properties of molecules and materials, yet it often remains a challenge to economically compute forces using this technique. As a result, ab initio molecular dynamics simulations and geometry optimizations that employ Diffusion Monte Carlo forces are often out of reach. One potential approach for accelerating the computation of "DMC forces" is to machine learn these forces from DMC energy calculations. In this work, we employ Behler-Parrinello Neural Networks to learn DMC forces from DMC energy calculations for geometry optimization and molecular dynamics simulations of small molecules. We illustrate the unique challenges that stem from learning forces without explicit force data and from noisy energy data by making rigorous comparisons of potential energy surface, dynamics, and optimization predictions among ab initio density functional theory (DFT) simulations and machine-learning models trained on DFT energies with forces, DFT energies without forces, and DMC energies without forces. We show for three small molecules─C2, H2O, and CH3Cl─that machine-learned DMC dynamics can reproduce average bond lengths and angles within a few percent of known experimental results at one hundredth of the typical cost. Our work describes a much-needed means of performing dynamics simulations on high-accuracy, DMC PESs and for generating DMC-quality molecular geometries given current algorithmic constraints.

Real-time hydrogen molecular dynamics satisfying the nuclear spin statistics of a quantum rotor

Apparent presence of the nuclear-spin species of a hydrogen molecule, para-hydrogen and ortho-hydrogen, associated with the quantum rotation is a manifestation of the nuclear quantum nature of hydrogen, governing not only molecular structures but also physical and chemical properties of hydrogen molecules. It has been a great challenge to observe and calculate real-time dynamics of such molecularized fermions. Here, we developed the non-empirical quantum molecular dynamics method that enables real-time molecular dynamics simulations of hydrogen molecules satisfying the nuclear spin statistics of the quantum rotor. While reproducing the species-dependent quantum rotational energy, population ratio, specific heat, and H-H bond length and frequency, we found that their translational, orientational and vibrational dynamics becomes accelerated with the higher rotational excitation, concluding that the nuclear quantum rotation stemmed from the nuclear spin statistics can induce various kinds of dynamics and reactions intrinsic to each hydrogen species.

Dynamic Interactions Between Brilliant Green and MscL Investigated by Solid-State NMR Spectroscopy and Molecular Dynamics Simulations.

The mechanosensitive ion channel of large conductance (MscL) is a promising template for the development of new antibiotics due to its high conservation and uniqueness to microbes. Brilliant green (BG), a triarylmethane dye, has been identified as a new antibiotic targeted MscL. However, the detailed binding sites to MscL and the dynamic pathway of BG through the MscL channel remain unknown. Here, the dynamic interactions between BG and MscL were investigated using solid-state NMR spectroscopy and molecule dynamics (MD) simulations. Residue site-specific binding sites of BG to the MscL channel were identified by solid-state NMR. In addition, MD simulations revealed that BG conducts through the MscL channel via residues along the inner surface of the pore sequentially, in which the strong hydrophobic interactions between BG and hydrophobic residues F23 and I27 in the hydrophobic gate region of the MscL channel are major restrictions. Particularly, it was demonstrated that BG activates the MscL channel by reducing the hydrophobicity of the F23 in the gate region by water molecules that are bound to BG. Taken together, these simulations and experimental data provide novel insights into the dynamic interactions between BG and MscL, based on which new hydrophobic antibiotics and adjuvants targeting MscL can be developed.