Molecular-level Simulations Research Articles

Molecular Structural Characteristics and 3D Model Reconstruction of Organic Matter in Longmaxi Formation Shale.

The establishment of molecular structure modeling is an important means to study the pore characteristics of shale organic matter and is significant for molecular-level simulations of gas storage and diffusion. Using 13C NMR, FTIR, and XPS combined with the split-peak fitting technique, the structural characteristics of the aromatic structure, aliphatic structure, and oxygen functional groups of kerogen from the shale of the Longmaxi Formation, Wuxi County, Chongqing Municipality, were quantitatively characterized. A macromolecular structure model of the kerogen was also constructed by using the 2D macromolecular structure model construction method in combination with elemental analysis experiments. The results showed that the 2D single-molecule structural model of the sample consisted of 2 benzenes, 2 naphthalenes, 1 anthracene, 5 pyrenes, 1 pyridine, and 1 pyrrole. The C skeleton types were 93 protonated arylons, 39 bridged arylons, 6 carboxylons, 5 alkyl-substituted carbons, 2 oxygen-substituted carbons, 4 methylene carbons, and 3 methylons. The established 2D molecular structure formula was C152H82O12N2. The final 3D macromolecular structure model consisted of 14 2D molecular structures (structural formula C2128H1148O168N28), with the density set to 1.77 cm3/g, compressed in a cubic cell with an edge length of 3.05 nm. Finally, the adsorption results showed that the experimental adsorption of CO2 adsorption was less than the simulated adsorption, completing the validation of the model. The above study provides a method for determining the molecular structure of kerogen in the Longmaxi Formation shale, which can guide the study of the pore structure characteristics of the Longmaxi Formation shale.

On exploiting nonparametric kernel-based probabilistic machine learning over the large compositional space of high entropy alloys for optimal nanoscale ballistics

The large compositional space of high entropy alloys (HEA) often presents significant challenges in comprehensively deducing the critical influence of atomic composition on their mechanical responses. We propose an efficient nonparametric kernel-based probabilistic computational mapping to obtain the optimal composition of HEAs under ballistic conditions by exploiting the emerging capabilities of machine learning (ML) coupled with molecular-level simulations. Compared to conventional ML models, the present Gaussian approach is a Bayesian paradigm that can have several advantages, including small training datasets concerning computationally intensive simulations and the ability to provide uncertainty measurements of molecular dynamics simulations therein. The data-driven analysis reveals that a lower concentration of Ni with a higher concentration of Al leads to higher dissipation of kinetic energy and lower residual velocity, but with higher penetration depth of the projectile. To deal with such conflicting computationally intensive functional objectives, the ML-based simulation framework is further extended in conjunction with multi-objective genetic algorithm for identifying the critical elemental compositions to enhance kinetic energy dissipation with minimal penetration depth and residual velocity of the projectile simultaneously. The computational framework proposed here is generic in nature, and it can be extended to other HEAs with a range of non-aligned multi-physical property demands.

Development of a universal atomistic cement model incorporating nanomaterials: From laboratory investigation to molecular simulation

Hydrated cement is considered one of the most complex binder systems. The development of models that suitably represent hydrated cement is still far from established due to the complexity of the hydration process, as well as the multiscale phase and different morphological properties. Therefore, this study attempts to develop a hydrated cement model using molecular-level simulation and laboratory testing. This model aimed to predict the mechanical properties of cement paste between 0.25 and 0.65 w/c ratios. In this regard, five cement paste mixtures with various w/c ratios were prepared and tested to evaluate their mechanical properties and micro-structures. Then, a novel modeling methodology using molecular dynamics (MD) simulation was carried out to develop an ingredient-based atomistic cement model. In this regard, the Dreiding force field (DFF) was used in all the simulation processes, starting from the cement clinker phases to the hydrated cement. The experimental results of cement paste mixtures revealed some useful data that was supportive of the simulations. Radial distribution function (RDF) and fractional free volume (FFV) were utilized to investigate the local atomic structure of the cement paste models. The calcium-silicate-hydrated (C–S–H) of the developed model was characterized through RDF and benchmarked to a well-known mineral analog, namely tobermorite. The developed model was computationally tested under different w/c ratios, and reliable results in terms of micro-structural matrix as well as mechanical properties were obtained. Then, the cement models incorporating different ratios of nano-silica were developed, and a good estimation of the mechanical properties was obtained. Because of the universal nature of the reported model in this research, it could be utilized to further predict the mechanical behavior and durability performance of the cement paste.

Explainable machine learning assisted molecular-level insights for enhanced specific stiffness exploiting the large compositional space of AlCoCrFeNi high entropy alloys

Design of high entropy alloys (HEA) presents a significant challenge due to the large compositional space and composition-specific variation in their functional behavior. The traditional alloy design would include trial-and-error prototyping and high-throughput experimentation, which again is challenging due to large-scale fabrication and experimentation. To address these challenges, this article presents a computational strategy for HEA design based on the seamless integration of quasi-random sampling, molecular dynamics (MD) simulations and machine learning (ML). A limited number of algorithmically chosen molecular-level simulations are performed to create a Gaussian process-based computational mapping between the varying concentrations of constituent elements of the HEA and effective properties like Young’s modulus and density. The computationally efficient ML models are subsequently exploited for large-scale predictions and multi-objective functionality attainment with non-aligned goals. The study reveals that there exists a strong negative correlation between Al concentration and the desired effective properties of AlCoCrFeNi HEA, whereas the Ni concentration exhibits a strong positive correlation. The deformation mechanism further shows that excessive increase of Al concentration leads to a higher percentage of face-centered cubic to body-centered cubic phase transformation which is found to be relatively lower in the HEA with reduced Al concentration. Such physical insights during the deformation process would be crucial in the alloy design process along with the data-driven predictions. As an integral part of this investigation, the developed ML models are interpreted based on Shapley Additive exPlanations, which are essential to explain and understand the model’s mechanism along with meaningful deployment. The data-driven strategy presented here will lead to devising an efficient explainable ML-based bottom-up approach to alloy design for multi-objective non-aligned functionality attainment.

Molecular Level Simulation of the Conversion and Distribution of Sulfides in the Fluid Catalytic Cracking and Hydrotreating Processes to Improve the Cleanliness of Gasoline and Diesel

Molecular Level Simulation of the Conversion and Distribution of Sulfides in the Fluid Catalytic Cracking and Hydrotreating Processes to Improve the Cleanliness of Gasoline and Diesel

Molecular-Level Kinetic Model for Light Hydrocarbon Steam Cracking Based on the SU-BEM Framework.

In this work, a molecular-level kinetic model of ethane/propane steam cracking was developed by using a hybrid structural unit-bond electron matrix framework. The molecular-level simulation was conducted, creating a detailed feedstock composition, formulating the reaction rules, and automating the generation and visualization of reaction networks. Ordinary differential equations were automatically generated based on the Arrhenius equation, while the kinetic parameters were reduced via linear free energy relations (LFERs). Furthermore, proper mathematical models for mass transfer, heat transfer, and momentum transfer within the cracking furnace were integrated into the molecular-level kinetic model, enabling the simultaneous calculation of the transfer process and chemical kinetics in steam cracking. The model was validated by its precise prediction of product yields, outlet pressure, and outlet temperature, which were collected from an industrial gas-cracking furnace.

A universal hydro-mechanical coupled behavior model for clay-bearing strata—Molecular-level simulation approach

Clay minerals in soils and rocks exhibit large volume change upon interaction with water and this behavior becomes even more complex when the strata are being stressed by the engineering and environmental loads. Therefore, a realistic prediction of the hydro-mechanical behavior of the clay-bearing strata is always a challenge due to their coupled swelling-mechanical response in the cases of geotechnical and geoenvironmental engineering problems, nuclear waste storage in clay-bearing rock repositories, shale gas extraction, and other uses of clay in the manufacturing industry. All the existing behavior models have restricted applications in the engineering and other fields of practice mainly due to the partial consideration of the structure and fabric of clay-bearing strata in the model formulation. In this study, a hydro-mechanical behavior model has been formulated using the parameters acquired from the molecular-level simulations and modeling of the volume change and stress–strain behavior of the clay-bearing structure. The Molecular Mechanics and Molecular Dynamic simulations were performed on the natural structure of the clay-bearing strata formulated using Monte Carlo technique. The mathematical model, developed from the simulation results, can predict the overall hydro-mechanical behavior of clay-bearing strata for all possible combinations of clay minerals, non-clay minerals, salts causing cementation of the soil/rock structure, confining pressures, and the induced strain levels. The developed model has successfully been validated through laboratory and field testing on the clay-bearing strata in both the elastic and elasto-plastic regions of the stress–strain behavior and also from the data of two (02) swelling clays (MX-80 and FEBEX Bentonite) from the existing literature, supporting the universal nature of the developed behavior model.

Molecular level investigation of interactions between pharmaceuticals and β-cyclodextrin (β-CD) functionalized adsorption sites for removal of pharmaceutical contaminants from water

This study describes a novel application of the use of molecular modeling tools for investigating the adsorption of organic micropollutants (OMPs) from water by nanocomposites. The partitioning of pharmaceuticals onto β-Cyclodextrin (β-CD) functionalized adsorbents was investigated at the molecular level to explore the atomistic interactions of pharmaceutical contaminants in water systems with β-CD and to provide insight into possible approaches for removal of pharmaceuticals from water. Molecular electrostatic surface potential mapping of β-CD derivatives was employed to examine the impact of substitution degree of β-CD and type of grafting agent on host-guest complexation. The stability of the complexes of selected pharmaceuticals and β-CD derivatives were assessed via molecular dynamics simulations to evaluate competitive adsorption between organic micropollutants (OMPs) and between OMPs and fulvic acid, a representative natural organic material (NOM) component found in water systems. Molecular electrostatic surface potential maps showed that grafting agents with aromatic and amine functional groups were found to provide attractive interactions for negatively charged OMPs. In addition, optimization of substitution degree of β-CD is necessary to enhance adsorption of target OMPs. Furthermore, it was found that aromatic ring bearing grafting agents can provide additional electrostatic attractions by π-π interactions with the aromatic ring of the OMPs. The impact of common water quality characteristics on adsorption was assessed and it was revealed that the effect of pH and calcium on adsorption depends on the ionizable functional groups present on the grafting agent. Molecular dynamics simulations showed that adsorption of target OMPs does not solely depend on hydrophobicity but is affected by electrostatic interactions. The simulations revealed that fulvic acid which is commonly present in environmental waters can be a competitor with ibuprofen for the β-CD cavity. Ultimately, this study showed that molecular level simulation can be effectively employed to investigate adsorption of OMPs by β-CD functionalized adsorbents and could be employed to enhance their design and subsequent environmental applications.

Potential energy surface, effect of solvents in molecular level, experimental spectra (FTIR, Raman, UV–visible & NMR), electronic, and dynamics simulation of isobavachalcone –Anti tuberculosis agent

The spectroscopic characteristics, molecular structure, and biological characteristics of the Isobavachalcone (IBC) structure have been studied using computational techniques utilizing the usual B3LYP/6-311++G (d,p). PED (Potential Energy Distribution) is used throughout process of assigning the fundamental wave-numbers of IBC. In this work, we have reported the correlation between experimental and computed examination of molecular configuration, FT:IR & Raman, UV–Visible absorption, and NMR (Nuclear Magnetic Resonance) spectrum of the title compound. Potential energy scan (PES) analysis of IBC was enhanced in the initial stage with same basis set. FMO (Frontier Molecular Orbital), MEP (Molecular Electrostatic Potential), local reactivity and quantum chemical descriptors has also been analyzed. Furthermore, drug-likeness and ADMET predictions of the present ligand shows good pharmacological activity. ELF (Electron Localised Function), LOL (Localised Orbital Locator) and RDG (Reduced Density Function) enacted for both the gas and solvents phases. A docking simulation performed with the 2FUM receptor using the Auto Dock software showed that the IBC compound has inhibitory activity against Mycobacterium tuberculosis disease. All our results suggest that the IBC molecule could have a great effect in the treatment of tuberculosis disease. MD simulations been executed over 100 ns to predict the RMSD, RMSF, and the radius-of gyration analyses.

Incoherent Energy Transfer between the Baseplate and FMO Protein Explored at Ideal Geometries.

The Förster resonance energy transfer (FRET) between the Fenna-Matthews-Olson (FMO) protein complex and the chlorosomal baseplate (CBP) is investigated by using an idealized model. This simplified model is based on crystal structure and molecular dynamics conformations. Some of the further input, such as the transition dipole moments, was extracted from earlier molecular-level simulations. The resulting model mimics the effects of the relative position between the CBP and the FMO complex on the corresponding FRET efficiency under ideal conditions, involving about 1.3 billion FRET calculations per investigated model. In this idealized model and employing some approximations, it is found that FRET efficiency is almost completely independent of the FMO trimer orientation (displacement, distance, and rotation), despite FMO and CBP being highly structured complexes. Even removing individual FMO BChl triples will only reduce the FRET efficiency by up to 8.6%. An FMO containing only the least efficient BChl triple will retain about 25% of the FRET efficiency of a full FMO complex. In addition to its proposed function as an energetic funnel, FMO is thus identified to act as a highly robust spatial funnel for CBP excitation harvesting, independent of the mutual CBP-FMO orientation.

Design of 8-mer peptides that block Clostridioides difficile toxin A in intestinal cells

Infections by Clostridioides difficile, a bacterium that targets the large intestine (colon), impact a large number of people worldwide. Bacterial colonization is mediated by two exotoxins: toxins A and B. Short peptides that can be delivered to the gut and inhibit the biocatalytic activity of these toxins represent a promising therapeutic strategy to prevent and treat C. diff. infection. We describe an approach that combines a Peptide Binding Design (PepBD) algorithm, molecular-level simulations, a rapid screening assay to evaluate peptide:toxin binding, a primary human cell-based assay, and surface plasmon resonance (SPR) measurements to develop peptide inhibitors that block Toxin A in colon epithelial cells. One peptide, SA1, is found to block TcdA toxicity in primary-derived human colon (large intestinal) epithelial cells. SA1 binds TcdA with a KD of 56.1 ± 29.8 nM as measured by surface plasmon resonance (SPR).

Tailoring ion dynamics in energy storage conductors for ultra-stable, high-performance solid-state microsupercapacitor array

All-solid-state electrochemical energy storage (EES) devices with prolonged lifetime, operational stability, and mechanical flexibility can be a promising route to powering up wearable electronics. However, conventional EES devices have been often hindered by a gradual decrease in energy capacity due to low ionic conducting electrolytes, non-suitable electrode materials, or poor electrode/electrolyte interfaces. Herein, we propose a harmonization of the molecular-level tailoring of ionic gel polymer electrolyte (IGPE) and graphene-based electrodes, significantly improving and sustaining the electrochemical performance (11.9 μWh cm−2) of EES microsupercapacitor (MSC) devices. Our optimized MSC device based on ethyl-3-methylimidazolium bis(fluorosulfonyl)imide (EMIM-FSI) and interdigitated reduced-graphene oxide (rGO) electrode array maintains 99 % of the initial electrochemical capacity even after 20,000 cycles, also demonstrating the excellent mechanical flexibility (bending radius up to 4 mm) and environmental stability (>30 days) of the MSC-based array. Molecular-level simulation and spectroscopic atomic analysis revealed noticeably lower residual ionic liquid (IL) molecules of EMIM-FSI, compared to EMIM-trifluoromethyl FSI, between the graphene-based layers of electrodes during the charge/discharge cycles. Therefore, our optimization strategy and findings will pave the way to accomplish next-generation of all-solid-state EES devices with ultra-high operational stability, powering wearable electronics.

CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks

Grand Canonical Monte Carlo is an important method for performing molecular-level simulations and assisting the study and development of nanoporous materials for gas capture applications. These simulations are based on the use of force fields and partial charges to model the interaction between the adsorbent molecules and the solid framework. The choice of the force field parameters and partial charges can significantly impact the results obtained, however, there are very few databases available to support a comprehensive impact evaluation. Here, we present a database of simulations of CO2 and N2 adsorption isotherms on 690 metal-organic frameworks taken from the CoRE MOF 2014 database. We performed simulations with two force fields (UFF and DREIDING), six partial charge schemes (no charges, Qeq, EQeq, MPNN, PACMOF, and DDEC), and three temperatures (273, 298, 323 K). The resulting isotherms compose the Charge-dependent, Reproducible, Accessible, Forcefield-dependent, and Temperature-dependent Exploratory Database (CRAFTED) of adsorption isotherms.

Binding mechanism of anacardic acid, carnosol and garcinol with PCAF: A comprehensive study using molecular docking and molecular dynamics simulations and binding free energy analysis.

The p300/CBP associated factor bromodomain (PCAF Brd) is emerged as one of the promising target proteins for different types of cancers. PCAF is one among the histone acetyltransferase enzymes which involved in the regulation of transcriptase process by modifying the chromatin structure. Anacardic acid, carnosol, garcinol are the experimentally reported inhibitors of PCAF Brd; however, their detailed binding mechanism these inhibitors are not yet known. The intermolecular interaction, binding energy, and the stability of these inhibitors with the active site of PCAF Brd are playing the key role in the binding of these inhibitors with PCAF. The in silico study incorporates the molecular docking and dynamics simulations; these molecular level simulations allow to understand the binding mechanism. In the present study, the induced fit molecular docking and molecular dynamics of anacardic acid, carnosol and garcinol molecules against the PCAF Brd have been performed. The docking score values of these molecules are -5.112 (anacardic acid), -5.141 (carnosol), -5.199 (garcinol) and -3.641 (L45) kcal/mol, respectively. Further, the molecular dynamics simulation was carried out for these docked complexes to understand their conformational their stability and binding energy from the roots means square deviation (RMSD) and root means square of fluctuation (RMSF), and molecular mechanics with the generalized born and surface area solvation (MM/GBSA) binding free energy calculations. The intermolecular interactions and binding free energy values confirm that garcinol forms key interactions and has high binding affinity towards PCAF Brd on compare with the other two inhibitors. Therefore, garcinol may be considered as a potential inhibitor of PCAF Brd.

Effect of thermal fluctuations on homogeneous compressible turbulence

For more than a century, it has been widely believed that there is a clear gap between molecular motions at the microscopic level and turbulent fluctuations at the macroscopic level. However, recent studies have demonstrated that the thermal fluctuations resulted from molecular motions have nonnegligible effects on the dissipation range of turbulence. To further clarify the reviving debate on this topic, we employ the molecular-level direct simulation Monte Carlo (DSMC) method to simulate homogeneous turbulence with different turbulent Mach numbers, extending the previous studies by considering the effect of compressibility. Our results show that, for both one-dimensional (1D) stationary turbulence and two-dimensional (2D) decaying isotropic turbulence, the turbulent energy spectra are significantly changed due to thermal fluctuations below the spatial scale comparable to the turbulent dissipation length scale. The energy spectra caused by thermal fluctuations for different spatial dimensions d present different scaling laws of the wavenumber k as {{k}^{(d-1)}}. For 2D cases, we show that the effect of thermal fluctuations on the spectrum of compressible velocity component is greatly affected by the change of compressibility. The 2D spectra of density, temperature and pressure are also obtained, showing the same scaling law at large wavenumbers as found for the energy spectra. Moreover, it is found that the effects of thermal fluctuations on the thermodynamic spectra are the same as those on the spectra of compressible velocity component.

Probing the molecular-level energy absorption mechanism and strategic sequencing of graphene/Al composite laminates under high-velocity ballistic impact of nano-projectiles

Motivated by recent discoveries concerning the extreme superiority of multilayer graphene in terms of kinetic energy dissipation compared to conventional monolithic materials, this article investigates the ballistic performance and physics-informed strategic sequencing of graphene-reinforced aluminum laminates under the influence of random disorder based on extensive molecular-level simulations of high-velocity impact. It is unraveled that strategic sequencing of graphene layers within the aluminum matrix can significantly enhance kinetic energy absorption, while preventing complete penetration. However, the reinforcement of bilayer graphene increases the projectile's post-impact residual velocity due to high magnitude of stress wave release provided by the reinforcement. We have further mitigated this effect to a significant extent by increasing the effective thickness of Al laminates. Based on the insights gained by a series of molecular-level simulations, we have proposed hybrid multifunctional laminates by coupling two individual configurations with high energy absorption and no penetration, respectively. By strategically providing higher graphene concentration near target surfaces, up to 90.77% of the kinetic energy can be absorbed. The findings of this study would be crucially useful in materializing the bottom-up multi-scale design pathway for producing graphene-reinforced Al composites to develop a novel class of functional barrier material-based engineered surfaces with improved nano-scale ballistic performance.

Minireview on Lattice Boltzmann Modeling of Gas Flow and Adsorption in Shale Porous Media: Progress and Future Direction

As an important unconventional resource, shale gas reservoirs have unique characteristics different from conventional oil and gas reservoirs. The ultrasmall pore sizes in shale induce the nanopore confinement effect on shale gas flow. In addition, shale rocks are rich in organic matter, which has strong interactions with gas molecules and results in gas adsorption. The lattice Boltzmann method (LBM) for micro- and nanoscale gas flow, which is originally designed for micro-electro-mechanical systems (MEMS), has been modified to simulate gas flow and adsorption in shale rocks. This work reviews four types of lattice Boltzmann models developed recently for shale gas flow/adsorption: (1) the slip-velocity-based LBM, (2) high-order LBM, (3) diffusion-based LBM, and (4) REV-scale LBM. Among these models, the slip-velocity-based LBM is widely used for shale gas modeling, which incorporates the slip boundary condition and Knudsen number (Kn)-determined relaxation time to simulate the nanopore confinement effect. To model the gas adsorption, the fluid–solid interaction force is introduced into the model, and the magnitude of this interaction force is usually obtained from the molecular level simulations. LBMs have been regarded as an efficient numerical tool to estimate the shale gas apparent permeability, to describe the pore-scale flow behaviors, and to address the influence of gas adsorption on shale gas storage and transport. Nevertheless, some challenges exist in current applications of LBMs for shale gas flow and adsorption simulations that are discussed in this minireview as well.

DEVELOPMENT OF COARSE-GRAINED MODELS OF LIQUID WATER BY DEEP NEURAL NETWORKS FOR SIMULATING ACOUSTIC VIBRATIONS OF NANOSTRUCTURES IN AQUEOUS ENVIRONMENT

Molecular-level simulation can effectively complement continuum analysis for the study on the damping mechanisms of acoustic vibrations of nanostructures in aqueous environment, which is central to the applications of nanostructures in high-sensitivity sensing and detection. It is highly desirable to develop coarse-grained (CG) water models that can accurately reproduce the density, compressibility, and viscosity of water simultaneously, for the molecular simulations of vibrations of nanostructures in water at affordable computational cost. In this work, the CG water models based on Lennard-Jones potential have been developed with each CG particle representing three and four water molecules. The deep neural networks have been trained using the data generated by CG molecular-dynamics simulations and used to solve the inverse problem of parameterization of the CG force fields for the target properties of water. As compared with many other existing CG models, the proposed CG water models are advantageous in terms of the ability to accurately predict the experimentally measured density, compressibility, and viscosity of water simultaneously, which is essentially important for the faithful molecular-level descriptions of the damping effect of the surrounding water on mechanical vibrations of nanostructures. Further comparisons suggest that the proposed three-to-one CG water model is a preferable option for molecular simulations of vibrations of nanostructures in water, due to its more accurate descriptions of target water properties.

In silicostudy of the binding of daunomycin and phenylalanine transfer RNA: probe molecular recognition for structure-based drug design

Structure-based design of drugs targeting RNAs relies on a systematic study of the molecular-level recognition mechanismviacomputational modelling and simulations.

Multiscale calculation of carrier mobility in organic solids through the fine-tuned kinetic Monte Carlo simulation

A multiscale approach is proposed to calculate carrier mobility (μ) in organic solids, bridging the molecular-level simulations and the kinetic Monte Carlo (kMC) method. The amorphous molecular supercell was prepared by the molecular dynamic simulation, then the density functional calculations were performed to obtain the relevant parameters for Marcus charge transfer between the constituent molecules. The on-lattice type kMC simulation implementing the Marcus transfer is systematically tuned to best mimic the profile of carrier transport in the simulated molecular supercell. The calculated μ readily reproduced the Pool-Frenkel field dependency, indicating that the present work well represents the typical behavior of carrier transport in organic systems. It is also shown that the calculation results of 4 anthracene-derivatives and 6 hole-transporting molecules agree well with the time-of-flight measurements from literature, while requiring the modest calculation cost in comparison with the similar calculations introduced elsewhere.