Molecular Dynamics Investigation Research Articles

A molecular dynamics investigation of laminar pyrocarbons elasticity up to high-temperatures

The mechanical properties of anisotropic carbons such as the pyrocarbon (pyC) matrices in C/C composites remain poorly documented, especially at elevated temperatures where these materials find most of their applications. We provide here a comprehensive molecular dynamics investigation of the high temperature – up to 4000 K – elastic behavior of six nanoscale pyC models in the context of fast temperature increases, not allowing for major structural modifications such as graphitization. We show that the structure of the most anisotropic and less disordered carbons, like the rough laminar (RL) pyC, is mostly not affected by annealing at the nanocecond timescale, aside from healing unstable defects like two-coordinated atoms at graphene edges. Conversely, highly disordered and less anisotropic carbons like the smooth laminar (SL) pyC show some significant rearrangements at grain boundaries and the development of some limited microporosity. The elastic constants of all highly anisotropic models moderately decrease with increasing temperature, somehow similarly to what is observed for graphite. Elastic constants of the SL pyC show a stronger decrease at high temperature, due to the decrease in density even though all models retain an important degree of stiffness up to 4000 K.

From Pixels to Druggable Leads: A CADD Strategy for The Design and Synthesis of Potent DDR1 Inhibitors

Background and Objective: While numerous in silico tools exist for target-based drug discovery, the inconsistent integration of in vitro with predictive models hinders research and development productivity. This is particularly apparent during the Hit-to-Lead stage, where unreliable in-silico tools often lead to suboptimal lead selection. Herein, we address this challenge by presenting a CADD-guided pipeline that successfully integrates rational drug design with in-silico hits to identify a promising DDR1 lead. Methods: 2 × 1000 ns MD simulations along with their respective FEL and MMPBSA analyses were employed to guide the rational design and synthesis of 12 novel compounds which were evaluated for their DDR inhibition. Results: The molecular dynamics investigation of the initial hit led to the identification of key structural features within the DDR1 binding pocket. The identified key features were used to guide the rational design and synthesis of twelve novel derivatives. SAR analysis, biological evaluation, molecular dynamics, and free energy calculations were carried out for the synthesized derivatives to understand their mechanism of action. Compound 4c exhibited the strongest inhibition and selectivity for DDR1, with an IC50 of 0.11 µM. Conclusions: The MD simulations led to the identification of a key hydrophobic groove in the DDR1 binding pocket. The integrated approach of SAR analysis with molecular dynamics led to the identification of compound 4c as a promising lead for further development of potent and selective DDR1 inhibitors. Moreover, this work establishes a protocol for translating in silico hits to real world bioactive druggable leads.

Comparing receptor binding properties of SARS-CoV-2 and of SARS-CoV virus by using unsupervised machine learning models

This work continues our recent molecular dynamics investigation of the three systems of the human ACE2 receptor interacting with the viral RBDs of SARS-CoV virus and two variants of SARS-CoV-2 viruses. The simulations are extended and analyzed using unsupervised machine learning models to give complementary descriptions of hidden features of the viral binding mechanism. Specifically, the principal component analysis (PCA) and the variational autoencoder (VAE) models are employed, both are classified as dimensionality reduction approaches with different focuses. The results support the molecular dynamics results that the two variants of SARS-CoV-2 bind stronger and more stable to the human ACE2 receptor than SARS-CoV virus does. Moreover, stronger bindings also affect the structure of the human receptor, making it fluctuate more, a sensitive feature which is hard to detect using standard analyses. Unexpectedly, it is found that the VAE model can learn and arrange randomly shuffled protein structures obtained from molecular dynamics in time order in the latent space representation. This result potentially has promising application in computational biomolecules. One could use this VAE model to jump forward in time during a molecular dynamics simulation, and to enhance the sampling of protein configuration space.

Nonequilibrium molecular dynamics investigation on friction behavior of organic friction modifiers under dynamic load

The evolution of lubricating oil containing additives under dynamic conditions is very significant, since its oil film structure, velocity distribution, and adsorption characteristic have an obvious impact on friction and wear. In this work, a boundary lubrication (BL) and elastohydrodynamic lubrication (EHL) models under sinusoidal load were carried out by nonequilibrium molecular dynamics (NEMD), using iron oxide as the substrate, hexadecane, and stearic acid molecules as lubricants. It was discovered that the friction in the BL regime is positively correlated with the degree of additive’s solidification. The greater the solidification degree of additive layers, the less the interlacing between them, making the relative motion between them easier and reducing compression oscillation within them, thus diminishing friction in the BL regime. The degree of liquefaction of base oil and additive regions, as well as the velocity difference within their interlacing zones, are connected to the friction force in the EHL regime. At high frequencies as well as large amplitudes, the oil film has a very high degree of liquefaction, which facilitates internal shear in it, and there is essentially no relative motion between the base oil and additives, resulting in low friction.

Acidic sphingomyelinase interactions with lysosomal membranes and cation amphiphilic drugs: A molecular dynamics investigation

Lysosomes are pivotal in cellular functions and disease, influencing cancer progression and therapy resistance with Acid Sphingomyelinase (ASM) governing their membrane integrity. Moreover, cation amphiphilic drugs (CADs) are known as ASM inhibitors and have anti-cancer activity, but the structural mechanisms of their interactions with the lysosomal membrane and ASM are poorly explored. Our study, leveraging all-atom explicit solvent molecular dynamics simulations, delves into the interaction of glycosylated ASM with the lysosomal membrane and the effects of CAD representatives, i.e., ebastine, hydroxyebastine and loratadine, on the membrane and ASM. Our results confirm the ASM association to the membrane through the saposin domain, previously only shown with coarse-grained models. Furthermore, we elucidated the role of specific residues and ASM-induced membrane curvature in lipid recruitment and orientation. CADs also interfere with the association of ASM with the membrane at the level of a loop in the catalytic domain engaging in membrane interactions. Our computational approach, applicable to various CADs or membrane compositions, provides insights into ASM and CAD interaction with the membrane, offering a valuable tool for future studies.

Thrombin - A Molecular Dynamics Perspective.

Thrombin is a crucial enzyme involved in blood coagulation, essential for maintaining circulatory system integrity and preventing excessive bleeding. However, thrombin is also implicated in pathological conditions such as thrombosis and cancer. Despite the application of various experimental techniques, including X-ray crystallography, NMR spectroscopy, and HDXMS, none of these methods can precisely detect thrombin's dynamics and conformational ensembles at high spatial and temporal resolution. Fortunately, molecular dynamics (MD) simulation, a computational technique that allows the investigation of molecular functions and dynamics in atomic detail, can be used to explore thrombin behavior. This review summarizes recent MD simulation studies on thrombin and its interactions with other biomolecules. Specifically, the 17 studies discussed here provide insights into thrombin's switch between 'slow' and 'fast' forms, active and inactive forms, the role of Na+ binding, the effects of light chain mutation, and thrombin's interactions with other biomolecules. The findings of these studies have significant implications for developing new therapies for thrombosis and cancer. By understanding thrombin's complex behavior, researchers can design more effective drugs and treatments that target thrombin.

Computational Screening of Some Phytochemicals to Identify Best Modulators for Ligand Binding Domain of Estrogen Receptor Alpha.

The peculiar aim of this study is to discover and identify the most effective and potential inhibitors against the most influential target ERα receptor by in silico studies of 45 phytochemicals from six diverse ayurvedic medicinal plants. The molecular docking investigation was carried out by the genetic algorithm program of AutoDock Vina. The molecular dynamic (MD) simulation investigations were conducted using the Desmond tool of Schrödinger molecular modelling. This study identified the top ten highest binding energy phytochemicals that were taken for drug-likeness test and ADMET profile prediction with the help of the web-based server QikpropADME. Molecular docking study revealed that ellagic acid (-9.3 kcal/mol), emodin (-9.1 kcal/mol), rhein (-9.1 kcal/mol), andquercetin (-9.0 kcal/mol) phytochemicals showed similar binding affinity as standard tamoxifen towards the target protein ERα. MD studies showed that all four compounds possess comparatively stable ligand-protein complexes with ERα target compared to the tamoxifen-ERα complex. Among the four compounds, phytochemical rhein formed a more stable complex than standard tamoxifen. ADMET studies for the top ten highest binding energy phytochemicals showed a better safety profile. Additionally, these compounds are being reported for the first time in this study as possible inhibitors of ERα for treating breast cancer, according to the notion of drug repurposing. Hence, these phytochemicals can be further studied and used as a parent core molecule to develop innovative lead molecules for breast cancer therapy.

Molecular Dynamics Investigations of Thermomechanical Characteristics of Solid and Hollow Spherical Platinum Nanoparticles during Additive Manufacturing

Molecular Dynamics Investigations of Thermomechanical Characteristics of Solid and Hollow Spherical Platinum Nanoparticles during Additive Manufacturing

Molecular Dynamics Investigation into CerENP's Effect on the Lipid Matrix of Stratum Corneum.

The extracellular lipid matrix in the stratum corneum (SC) plays a critical role in skin barrier functionality, comprising three primary components: ceramides, cholesterol, and free fatty acids. The diverse ceramides, differentiated by molecular structures such as hydroxylations and varying chain lengths, are essential for the lipid matrix's structural integrity. Recently, a new subclass of ceramide, 1-O-acylceramide NP (CerENP), has been identified; however, its precise role in the lipid matrix of the SC is still elusive. Herein, we investigate the role of CerENP on the structure and permeability of the SC using molecular dynamics simulations. Our findings indicate that CerENP contributes to a compact lipid matrix in the lateral dimension of our SC model with a repeat distance of about 13 nm. Additionally, ethanol permeability assessments show that CerENP effectively reduces molecular penetration through the lipid matrix. This study provides an insight into the role of a new subclass of ceramide in the SC, enhancing our understanding of skin structure and the mechanisms behind barrier dysfunction in skin diseases.

Ab initio Molecular Dynamics Investigation on the Permeation of Sodium and Chloride Ions through Nanopores in Graphene and Hexagonal Boron Nitride Membranes.

Nanoporous membranes promise energy-efficient water desalination. Hexagonal boron nitride (h-BN), like graphene, exhibits outstanding physical and chemical properties, making it a promising candidate for water treatment. We employed Car-Parrinello molecular dynamics simulations to establish an accurate modeling of Na+ and Cl- permeation through hydrogen passivated nanopores in graphene and h-BN membranes. We demonstrate that ion separation works well for the h-BN system by imposing a barrier of 0.13 eV and 0.24 eV for Na+ and Cl- permeation, respectively. In contrast, for permation of the graphene nanopore, the Cl- ion faces a minimum of energy of 0.68 eV in the nanopore plane and is prone toward blockade of the nanopore, while the Na+ ion experiences a slight minimum of 0.03 eV. Overall, the desalination performance of h-BN nanopores surpasses that of their graphene counterparts.

Robust Algorithms for the Analysis of Fast-Field-Cycling Nuclear Magnetic Resonance Dispersion Curves

Fast-Field-Cycling (FFC) Nuclear Magnetic Resonance (NMR) relaxometry is a powerful, non-destructive magnetic resonance technique that enables, among other things, the investigation of slow molecular dynamics at low magnetic field intensities. FFC-NMR relaxometry measurements provide insight into molecular motion across various timescales within a single experiment. This study focuses on a model-free approach, representing the NMRD profile R1 as a linear combination of Lorentzian functions, thereby addressing the challenges of fitting data within an ill-conditioned linear least-squares framework. Tackling this problem, we present a comprehensive review and experimental validation of three regularization approaches to implement the model-free approach to analyzing NMRD profiles. These include (1) MF-UPen, utilizing locally adapted L2 regularization; (2) MF-L1, based on L1 penalties; and (3) a hybrid approach combining locally adapted L2 and global L1 penalties. Each method’s regularization parameters are determined automatically according to the Balancing and Uniform Penalty principles. Our contributions include the implementation and experimental validation of the MF-UPen and MF-MUPen algorithms, and the development of a “dispersion analysis” technique to assess the existence range of the estimated parameters. The objective of this work is to delineate the variance in fit quality and correlation time distribution yielded by each algorithm, thus broadening the set of software tools for the analysis of sample structures in FFC-NMR studies. The findings underline the efficacy and applicability of these algorithms in the analysis of NMRD profiles from samples representing different potential scenarios.

Interfacial Optimization for AlN/Diamond Heterostructures via Machine Learning Potential Molecular Dynamics Investigation of the Mechanical Properties.

AlN/diamond heterostructures hold tremendous promise for the development of next-generation high-power electronic devices due to their ultrawide band gaps and other exceptional properties. However, the poor adhesion at the AlN/diamond interface is a significant challenge that will lead to film delamination and device performance degradation. In this study, the uniaxial tensile failure of the AlN/diamond heterogeneous interfaces was investigated by molecular dynamics simulations based on a neuroevolutionary machine learning potential (NEP) model. The interatomic interactions can be successfully described by trained NEP, the reliability of which has been demonstrated by the prediction of the cleavage planes of AlN and diamond. It can be revealed that the annealing treatment can reduce the total potential energy by enhancing the binding of the C and N atoms at interfaces. The strain engineering of AlN also has an important impact on the mechanical properties of the interface. Furthermore, the influence of the surface roughness and interfacial nanostructures on the AlN/diamond heterostructures has been considered. It can be indicated that the combination of surface roughness reduction, AlN strain engineering, and annealing treatment can effectively result in superior and more stable interfacial mechanical properties, which can provide a promising solution to the optimization of mechanical properties, of ultrawide band gap semiconductor heterostructures.

A molecular dynamics study of thin water layer boiling on a plate with mixed wettability and nonlinearly increasing wall temperature

This paper introduces a molecular dynamics investigation of the influence of mixed-wettability on the boiling of a water layer over a flat plate surface with nonlinearly increasing wall temperature. The first-type Dirichlet temperature condition, which is considered for the first time in the analysis of the wettability influence on nucleation and boiling at the atomic scale, is consistent with the case of irradiation from photons, plasma, or high-temperature gas molecules. The simulation of the density evolution of water molecules in the nucleation zone shows that the surface with an optimal proportion (60 %∼70 %) of hydrophilic walls is most efficient for nucleation on the studied mixed-wettability substrates, attributing to the increased surface potential energy on the walls. Compared to the hydrophilic or hydrophobic surfaces, the mixed-wettability surfaces transfer more energy from the wall to the liquid. This is because the hydrophobic portion retards the forming of vapor layer and the hydrophilic portion induces an efficient liquid/solid interaction, with a lowered interfacial heat resistance and promoted nucleation. The results also indicate that there exists an appropriate area ratio that is most advantageous for nucleation and boiling intensification under such temperature-increasing boundary conditions. Based on this work, it may be possible to successfully manage boiling at the nanoscale by exploiting the coupled effects of hybrid wettability and irradiation.

Molecular dynamics investigations on the thermophysical property of fatty acid ethyl esters

To develop innovative fuel additives or substitutes and design spray-assisted internal combustion engines, it is important to have a comprehensive understanding of the thermophysical characteristics of biodiesels. Rooted in statistical mechanics, the molecular simulation method can predict the thermophysical properties over a wide range of temperatures and pressures, which overcomes the challenges of experimental measurements and facility restrictions. This work focused on the evaluation of three classical force fields (FFs) in terms of their ability to predict the thermophysical properties of ethyl-caprylate (EtC8:0), ethyl-caprate (EtC10:0), ethyl-laurate (EtC12:0), ethyl-myristate (EtC14:0), ethyl-palmitate (EtC16:0), and ethyl-stearate (EtC18:0) within a temperature range of (303.15 to 403.15) K and pressures up to 100 MPa. The Optimized Potential for Liquid Simulations–All atom and its modification (OPLS and L–OPLS) and Transferable Potential for Phase Equilibria–United Atom (TraPPE–UA) FFs were evaluated. These findings demonstrated that the L–OPLS FF, when applied in the isobaric-isothermal (NPT) and canonical ensembles (NVT), effectively replicated the experimental data and semi-empirical models for density, viscosity, diffusivity coefficient, and interfacial behavior at elevated temperatures and pressures. Moreover, the utilization of the radial distribution function, radius of gyration, and end-to-end distance distribution in the analysis of liquid conformations allowed for a comprehensive investigation of the microscopic structure. This work provides the theoretical methodologies and empirical evidence for the advancement of biodiesel and other petrochemicals.

Study on the effects of inorganic salts and ionic surfactants on the wettability of coal based on the experimental and molecular dynamics investigations

Through the evaluation and preparation of a novel inorganic salt composite solution consisting of sodium dodecyl benzene sulfonate (SDBS), the nonionic surfactants polyethylene glycol pisooctyl phenyl ether (Triton X-100), and inorganic salt Na2SO4, based on surface tension, contact angle, settling time, wetting rate, and changes in the surface microstructure and functional groups of coal, this study analyzed the wetting behavior of the solution on coal dust and its adsorption capacity on the surface of coal particles. The results demonstrate that the composite solution of inorganic salts can significantly enhance the wettability of coal samples, reducing the contact angle to 21.54° and lowering the surface tension to 26.10 mN/m. After treatment with the solution, the relative content of oxygen-containing functional groups in the microscopic structure of the coal sample increased to 59.6 % and pores more prominent than 25 nm increased to 34.63 %. Molecular dynamics (MD) simulations of the water-inorganic salt composite solution-coal system are conducted using Materials Studio software, revealing that after the addition of the composite solution, the self-diffusion coefficient (D) of water molecules is 6.3 × 10-9 m2/s, the starting point of the composite solution is 15 Å, and the system's interaction energy increase to 14000 kcal/mol. It further elucidated the modification of the surface molecular structure of coal dust by the inorganic salt composite solution, increasing hydrogen bonds, enhancing wetting, and demonstrating a solid adsorption capability of the inorganic salt composite solution on the surface of coal dust.

Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition

Vibrational spectroscopy allows us to understand complex physical and chemical interactions of molecular crystals and liquids such as ammonia, which has recently emerged as a strong hydrogen fuel candidate to support a sustainable society. We report inelastic neutron scattering measurement of vibrational properties of ammonia along the solid-to-liquid phase transition with high enough resolution for direct comparisons to ab-initio simulations. Theoretical analysis reveals the essential role of nuclear quantum effects (NQEs) for correctly describing the intermolecular spectrum as well as high energy intramolecular N-H stretching modes. This is achieved by training neural network models using ab-initio path-integral molecular dynamics (PIMD) simulations, thereby encompassing large spatiotemporal trajectories required to resolve low energy dynamics while retaining NQEs. Our results not only establish the role of NQEs in ammonia but also provide general computational frameworks to study complex molecular systems with NQEs.

An ab initio molecular dynamics investigation of the behaviour of amorphous substances in anodic aluminium oxide under electric field

In order to elucidate the diffusion behaviour of ions in alumina during the anodic alumina process, the effects of electric field strength, hydration content, and electrolyte on amorphous alumina and hydrated alumina were studied using ab initio molecular dynamics. The results show that the diffusion rate of ions in alumina increases with the increase in electric field strength, but there is an extreme value. The maximum diffusion rate of Al ions in alumina monohydrate is 21.8 μm2/ms/V, while in alumina trihydrate, it is 16.7 μm2/ms/V. The ionic diffusion rate of hydrated alumina is one to two orders of magnitude larger than that of anhydrous amorphous alumina due to the effect of the drag of H ions, which reduces the migration activation energy. Electrolytes also affect the diffusion rate of alumina through the action of H ions. The increase in H ions will not only enhance the diffusion rate of hydrated alumina but also render the hydrous compound more vulnerable to breakdown.

Molecular dynamics investigation of wear mechanisms in drilling motion for FeCrNiCoAl high-entropy alloy

This work employed molecular dynamics (MD) to investigate the wear mechanism in the drilling motion of FeCrNiCoAl high entropy alloy. MD simulations reveal a reduction in tangential force upon applying torque to the tool, attributed to the synergy between rotational and thermal effects. Entropy generation analysis indicates that below 20 GHz, rotational effects prevail, while conversely, thermal effects take precedence. Furthermore, the correlation between wear volume and rotation frequency exhibits non-monotonic characteristics, which suggests that fine-tuning material wear can be achieved through adjustments to the rotation frequency. An in-depth analysis of the strain energy and the Bejan number allows the categorization of wear into three modes: rotation-dominant, transitional, and thermal-dominant.

Molecular Dynamics Investigations of Dienolate [4 + 2] Reactions.

We report quantum mechanics calculations and quasiclassical trajectory simulations of [4 + 2] reactions using three common dienolate substrates: siloxy dienes, Li dienolates, and conjugated Pd enolates. Asynchronous transition structures and unequal bond formation were invariably found, with average time gaps of developing bonds ranging from 26.5 to >251.0 fs. The results display a spectrum of dynamically concerted and stepwise [4 + 2] reactions, offering insights into the origin of the stereochemical outcomes of such reactions.

Reactive Force Field Molecular Dynamics Investigation of NH3 Generation Mechanism during Protein Pyrolysis Process.

The mechanism of ammonia formation during the pyrolysis of proteins in biomass is currently unclear. To further investigate this issue, this study employed the AMS 2023.104 software to select proteins (actual proteins) as the model compounds and the amino acids contained within them (assembled amino acids) as the comparative models. ReaxFF molecular dynamics simulations were conducted to explore the nitrogen transformation and NH3 generation mechanisms in three-phase products (char, tar, and gas) during protein pyrolysis. The research results revealed several key findings. Regardless of whether the model compounds are actual proteins or assembled amino acids, NH3 is the primary nitrogen-containing product during pyrolysis. However, as the temperature rises to higher levels, such as 2000 K and 2500 K, the amount of NH3 decreases significantly in the later stages of pyrolysis, indicating that it is being converted into other nitrogen-bearing species, such as HCN and N2. Simultaneously, we also observed significant differences between the pyrolysis processes of actual proteins and assembled amino acids. Notably, at 2000 K, the amount of NH3 generated from the pyrolysis of assembled amino acids was twice that of actual proteins. This discrepancy mainly stems from the inherent structural differences between proteins and amino acids. In proteins, nitrogen is predominantly present in a network-like structure (NH-N), which shields it from direct external exposure, thus requiring more energy for nitrogen to participate in pyrolysis reactions, making it more difficult for NH3 to form. Conversely, assembled amino acids can release NH3 through a simpler deamination process, leading to a significant increase in NH3 production during their pyrolysis.