Journal of Molecular Modeling最新文献

Context

This study comprehensively describes the interaction between SiO₂ spherical nanoparticles and water molecules as a solvent medium. Our goal is to provide valuable insights into the significance of nanoparticle size in understanding their behavior and the resulting changes in the physical properties of materials. Our results indicate that SiO₂ nanoparticles exhibit a strong affinity for water, which increases with the nanoparticle size. Our investigation can be relevant for the design of new composite materials with applications ranging from medical prostheses to quantum electronics, optoelectronic devices, catalysis, and photoluminescence. We have concentrated on the study of the amorphous, where size effects seem to be more pronounced.

Methods

A computational study was carried out within the molecular dynamics simulations framework available in the GROMACS-v2019.2 software, with force fields consistent with DFT and the CHARMM36 utilized in the molecular description of the systems. The water model used was the TIP3P implemented in CHARMM36 force fields. A comprehensive analysis of molecular interactions of various system configurations was performed, including radial distribution function (RDF), mean square displacement (RMSD), hydrogen bonding analysis, interfacial analysis, and studying system size's effect on mechanical properties.

Context

Microstamping has been shown to enhance the surface strength of alloy materials by improving interatomic density. This paper delves into the damage mechanism of TiAl6V4(TC4), which has been processed using high-speed stamping with varying overlap ratios at the atomic level. Additionally, the general trend of stress variation between loading and unloading is discussed. The mechanical properties of the substrate and the changes in microstructure resulting from varying overlap rates in microstamping were investigated. The impact of different machining overlap ratios on the depth of the damaged layer, the number of dislocation density lines, and the density of the matrix is also explored. The results indicate that the dislocation density remains relatively unchanged due to material hardening, while the overlap ratio increases continuously. Based on this analysis, a more optimal microstamping overlay ratio parameter is proposed to effectively enhance the surface strength of the substrate and reduce processing time.

Method

First, an alloy model with titanium, aluminum, and vanadium was created in ATOMSK and LAMMPS software. The model was divided into three layers: fixed, constant temperature, and Newton. To ensure the accuracy of the simulation, the system was annealed in order to minimize energy and replicate real-world conditions. Zhou’s EAM alloy potential was employed to represent the interaction between the alloy atoms, while the Tersoff potential was used to represent the interatomic interaction of the diamond indenter. Additionally, the LJ potential function was selected to depict the interaction between the metal atom and the diamond indenter. The construct surface mesh method in OVITO software was then utilized to construct a surface mesh and analyze the impact of different machining overlap rates on surface topography. The common neighborhood analysis (CNA) module in OVITO was used to calculate the number of defective atoms and the depth of the damaged layer. Finally, the DXA (dislocation extraction analysis) module in OVITO was used to calculate the dislocation density length and dislocation density.

Context

BCC and FCC metals have different glass-forming abilities (GFA) and exhibit different characteristics during the glass transition. However, the structural origin of their different GFAs is still not clear. Here, we explored the structures of eight monatomic metallic glasses by combining molecular dynamics (MD) simulations and machine learning (ML). Our findings reveal that, despite their common long-range disordered atomic structure, metallic glasses can be further classified into two distinct categories indicating an underlying structural order within the disorder. Using machine learning, we found that BCC liquids can sample more diverse glass states than FCC liquids. Furthermore, glasses formed from BCC metals (GFFBs) exhibit a higher degree of disorder than glasses formed from FCC metals (GFFFs). These findings highlight the inherent differences between GFFFs and GFFBs, which help explain the different glass-forming abilities of FCC and BCC metals. Additionally, our results demonstrate the promising potential of computer vision and ML methods in exploring material structures.

Method

Classical molecular dynamics simulations were employed to generate configurations of GFFBs and GFFFs, and the simulations were performed using the LAMMPS code. Inter-atomic interactions were described using a classical embedded atom model (EAM) potential. The initial configuration of the model consists of 32,000 atoms in a three-dimensional (3D) cubic box with periodic boundary conditions applied in all three directions. For machine learning, we utilized an unsupervised machine learning method along with MobileNetV2 for classifying glass structures. Image entropy and image distances were used to measure the structural differences of the metallic glasses.

Context

Advanced copper and copper alloys, as significant engineering structural materials, have recently been extensively used in energy, electron, transportation, and aviation domains. Higher requirements urge the emergence of high-performance copper alloys. However, the traditional trial-and-error experimental observations and computational simulation research used to design and develop novel materials are time-consuming and costly. With the accumulation of material research and rapid development of computational ability, the thorough application of material genome engineering has sped up the development of novel materials and facilitates the process of systematic engineering application.

Methods

This review summarizes the benefits of data-driven machine learning techniques and the state of the art of machine learning research in the area of copper alloys. It also displays the widely used computational simulation approaches (e.g., the first-principles calculation, molecular dynamics simulation, phase-field simulations, and finite element analysis) and their combined applications in material design and property prediction. Finally, the limitations of machine learning research methods are outlined, and future development directions are proposed.

Context: The monolayers of ethylene glycol and 2-hydroxyethoxide on gold surfaces have been used in hybrid materials as biosensors. In this article, the adsorption of ethylene glycol and 2-hydroxyethoxide on the Au(111) surface was analyzed. For the first system, ethylene glycol on Au(111), there are Au(cdot cdot cdot )O and Au(cdot cdot cdot )H interactions. To the best of our knowledge, the Au(cdot cdot cdot )H interaction has been overlooked until now. However, in this work, there is strong evidence that this interaction is important to stabilize the system. For the second system, the atomic interactions mentioned previously are also predicted, although there is an additional interaction between 2-hydroxyethoxide molecules. Such an interaction induces the link -O-H-O-, with high values of the electron density at the critical points of the corresponding bond path of the O-H interaction. These links suggest the forming of ethylene glycol chains. Methods: The calculations were performed using two exchange-correlation functionals: BEEF-vdW and C09(_{x})-vdW; both functionals incorporate dispersion effects within the Kohn-Sham approach in Density Functional Theory as implemented in GPAW code and ASE computational packages. The contacts between the molecules considered in this article and the Au(111) surface were analyzed through the Quantum Theory of Atoms in Molecules implemented in GPUAM code.

Context

Based on the density functional theory (DFT), we analyzed the changes of the FOX-7 molecule under external electric field (EEF) from multiple perspectives, including molecular structure, electronic structure, decomposition mechanism, frontier molecular orbitals (FMOs), and density of states (DOS). The results revealed that as the intensity of the positive EEF increased, the detonation performance of the FOX-7 molecule was significantly enhanced, while its thermal stability was also improved. This discovery challenges the traditional concept that explosives are inherently dangerous under external electric fields and provides new insights for research in related fields. To further explore the impact of EEF on the thermal stability of FOX-7, we conducted a thorough analysis of the mechanism by which EEF affects the decomposition process. Our findings indicate that applying a positive EEF significantly increases the energy required to overcome intramolecular hydrogen transfer and C-NO₂ bond rupture, while having a relatively minor effect on the nitro isomerization process. This observation further demonstrates that the appropriate application of a positive EEF can enhance the detonation performance of FOX-7 without compromising its thermal stability. Further research revealed that as the intensity of the positive EEF increased, the electronegativity of the nitro group gradually enhanced, leading to an increase in the electronegativity of the oxygen atoms within it. This made the oxygen atoms more prone to participating in chemical reactions. This phenomenon also explains why the energy barrier required for nitro isomerization in FOX-7 gradually decreases as the intensity of the positive EEF increases.

Methods

Based on the density functional theory (DFT), the structural optimizations were performed both under applied EEF and without EEF at the B3LYP/6-311G (d, p) level. All optimized results were converged and exhibited no imaginary frequencies. Based on the optimized structures, single-point energy calculations were further conducted at the B3LYP/def2-TZVPP level. Subsequently, analyses of molecular structure, electronic structure, decomposition mechanism, frontier molecular orbitals, and density of states were carried out.

Context

The propargyl radical plays a critical role in various chemical processes, including hydrocarbon combustion, flame synthesis, and interstellar chemistry. Its unique stability arises from the delocalization of π-electrons, allowing it to participate in a wide range of reactions despite being a radical. The self-reaction of propargyl radicals is a fundamental step in synthesizing polycyclic aromatic hydrocarbons. In this work, therefore, a computational study into the C₃H₃ + C₃H₃ potential energy surface has been carefully characterized. The calculated results indicate that the reaction can occur by H-abstraction or addition of two propargyl radicals together. The H-abstraction mechanism can create the products P3 (H₂CCC + H₃CCCH) and P4 (H₂CCCH₂ + HCCCH) but the energy barriers of the two H-abstraction channels are very high (from 12 to 22 kcal/mol). In contrast, the addition mechanism of two propargyl radicals forming the intermediates (I₁, I₅, I₁₂) and the bimolecular products (P1, P2, P7, P11, P12) are dominant. Among the bimolecular products, the P11 (C₆H₄ + H₂) product is the most energetically favorable, and the channel leading to this product is also the most preferred path compared to all other paths throughout the PES. The calculated enthalpy changes of various reaction paths in this study are in good agreement with the available literature data, indicating that the CCSD(T) method is suitable for the title reaction. The overall rate constant of the reaction depends on both temperature and pressure, reducing with temperature but rising with pressure. The calculated results agree closely with the available experimental values ​​and previous calculated data. Thus, it can be affirmed that in addition to the CASPT2 method as applied in the study of Georgievskii et al. (Phys. Chem. Chem. Phys., 2007, 9, 4259–4268), the CCSD(T) method is also very good for the self-reaction of two propargyl radicals.

Methods

The M06-2X and CCSD(T) methods with the aug-cc-pVTZ basis set were used to optimize and calculate single-point energies for all species of the reaction. The bimolecular rate constants of the dominant reaction paths were predicted in the temperature and pressure ranges of 300–1800 K and 0 – 76,000 Torr, respectively, using the VTST and RRKM models with Eckart tunneling correction for the H-shift steps.

Context

This study investigates the potential of leveraging molecular properties, as determined by MD-LOVIs software and machine learning techniques, to predict the ability of compounds to cross the blood–brain barrier (BBB). Accurate prediction of BBB permeation is critical for the development of central nervous system (CNS) drugs. The study applies various machine learning models, including both classification and regression techniques, to predict BBB passage and molecular activity. Notably, classification models such as GBM-AWV (accuracy = 0.801), GLM-CN (accuracy = 0.808), SVMPoly-means (accuracy = 0.980), SVMPoly-AC (accuracy = 0.980), SVMPoly-MI_TI_SI (accuracy = 0.900), SVMPoly-GI (accuracy = 0.900), RF-means (accuracy = 0.870), and GLM-means (accuracy = 0.818) demonstrate high accuracy in predicting BBB passage. In contrast, regression models like ES-RLM-AG (R² = 0.902), IB-IBK (R² = 0.82), IB-Kstar (R² = 0.834), IB-MLP (R² = 0.843), and DRF-AWV (R² = 0.810) exhibit strong performance in predicting molecular activity. The results show that classification models like GBM-AWV, GLM-CN, and SVMPoly variants, as well as regression models like ES-RLM-AG and IB-MLP, achieve high performance, demonstrating the effectiveness of machine learning in predicting BBB permeability.

Methods

The computational methods employed in this study include the MD-LOVIs software for generating molecular descriptors and several machine learning algorithms, including gradient boosting machines (GBM), generalized linear models (GLM), support vector machines (SVM) with polynomial kernels, random forests (RF), ensemble regression models, and instance-based learning algorithms. These models were trained and validated using various datasets to predict BBB passage and molecular activity, with the performance metrics reported for each model. Standard computational techniques were employed throughout, ensuring the reliability of the predictions.

Graphical Abstract

Context

Based on the methods of quantum chemistry and atom–atom potentials, the molecular and crystal structure of a number of high-energy pyrazines was modeled: unsubstituted diazines, as well as fully nitrated 1,4-diazabenzenes, their oxides and polymorphs. The enthalpies of formation, densities of molecular crystals, and some performance characteristics of these compounds were determined. The parameters of decomposition of substances were estimated. It has been established that tetranitropyrazine-1,4-dioxide has maximum energy content and excellent performance characteristics, which determine the prospects for using this compound as a high-energy one in the considered series of compounds.

Methods

In this work, DFT calculations were conducted through the software Gaussian 09 using B3LYP functional with basis set aug-cc-PVDZ and the Grimme dispersion correction D2. For crystal structure optimization, the atom–atom potential methods with PMC program (Packing of Molecules in Crystal) were used. Charges for molecular electrostatic potential were fitted by FitMEP and enthalpies of formation in gas phase were assessed by G3B3.