Journal of Molecular Modeling最新文献

Context

In this work, we explore a novel analogy between the classical capacitor from electrostatics and the linear response function within the framework of conceptual density functional theory (CDFT). Parallels are drawn between the electrostatic behavior of capacitors and the chemical reactivity described by the linear response function, a key descriptor in CDFT. This analogy is illustrated on molecular systems ranging from diatomics to four-atom molecules, and generalized to larger systems. We further show how this relationship extends to other chemical descriptors, offering new physical interpretations. The results demonstrate that this capacitor analogy provides fresh insights into chemical reactivity and enriches the conceptual framework of theoretical chemistry.

Methods

All calculations were performed using the ADF package. Molecules were optimized in the gas phase at the PBE0/TZP level, including scalar relativistic effects, with convergence verified by positive vibrational frequencies. Conceptual DFT descriptors, including the condensed linear response function, were obtained using the standard implementation in ADF. An in-house Python program was developed to extract and visualize condensed linear response data, perform diagonalizations, and generate graphical representations of eigenmodes.

Context

This study aims and objectives of the present research are to provide fundamental insights into their electronic properties and adsorption behavior, and predict the corrosion inhibition performance of seven nucleobase-derived organic compounds—adenine (S1), cytosine (S2), glutamine (S3), guanine (S4), purine (S5), pyrimidine (S6), and thymine (S7)—on Cu, Fe, Al, and Sn metal surfaces. Results revealed that cytosine (S2) and guanine (S4) exhibit the highest inhibition efficiency on Cu surfaces, driven by their small HOMO–LUMO energy gap (~ 5.00, ~ 5.10 eV), high softness (0.203, 0.198 eV⁻¹), and molecule-to-metal electron charge transfer (0.193, 0.237), indicating strong adsorption and electron exchange capabilities from the HOMO_donor ⟶ LUMO_acceptor. The importance of the results highlights the potential of these compounds for developing sustainable corrosion inhibitors and advanced Cu-based anticorrosive coatings and biosensors.

Methods

Using density functional theory (DFT) at the B3LYP/6–311 + + G(d,p) level of theory with applying Gaussian 09, Revision D.01, to optimize the structures combined with Monte Carlo simulations applied to point out interactions surface, and evaluated adsorption energies on Fe (110), Sn (111), Cu (111), and Al (111) surfaces of S1–S7 title compounds. Topological analyses, including electron localization function (ELF), localized orbital locator (LOL), and density of states (DOS), were performed using Multiwfn software to understand electron distribution and bonding characteristics. The comprehension and knowledge gathered from these techniques can help create a prediction of nucleobase-derived capacity to suppress corrosion inhibitors that are both sustainable and effective.

Graphical Abstract

Context

Lipid oxidation degrades food quality by producing harmful aldehydes, ketones, and acids. This study integrates density functional theory (DFT) and kinetic analysis to elucidate the thermal oxidation mechanism of oleic acid using a 4-octene model. Mechanistic insights reveal distinct early- and mid-stage processes: the initial phase is dominated by low-barrier allyl-oxygen bonding (< 10.0 kcal/mol), while allyl isomerization (> 60.0 kcal/mol) is negligible. Deep oxidation involves four pathways: 1) O–O• radical attack on adjacent carbons (29.2/11.3 kcal/mol), forming aldehydes; 2) peroxy radical isomerization (rate-limiting carbon jump > 30.0 kcal/mol); 3) epoxide formation via O–O• attack on double bonds (6.8 kcal/mol); 4) hydroperoxide conversion hindered by high barriers (57.3–58.7 kcal/mol). Alkoxy radicals induce C–C bond cleavage, forming minor cyclic epoxides. Kinetic analysis (60 and 150 °C) identifies tricyclic epoxide as the dominant product, stabilized at elevated temperatures.

Methods

This article uses density functional theory (DFT) and Gaussian 16 program to complete structural optimization, frequency analysis, and single point energy calculation. B3LYP-D3/6-31G (d, p) method is used for optimization and frequency calculation, and single point energy correction is performed using the def2TZVPP basis set. The transition state has been verified by IRC, and the free energy has been corrected by Shermo program zero-point energy. The reaction rate constant is based on the transition state theory (TST) and takes into account the tunneling effect, and is calculated using the TST calculator program. Finally, the differential equation system is solved using MATLAB to obtain the time concentration curve of the oxidation product.

Context

This study investigates the thermal degradation behavior of cis-1,4-polyisoprene and its nanocomposites using a combined approach of reactive molecular dynamics simulations and experimental techniques. Pyrolysis was experimentally conducted up to 500 °C using a custom-built lab-scale-tubular reactor (20 mm diameter, 300 mm length). Samples were prepared by melt mixing, followed by compression molding into 1 mm thick sheets. Simulations were performed up to 2500 K using the ReaxFF reactive force field, selected for its ability to dynamically model bond breaking and formation in reactive systems. We constructed a simulation system containing ten polymer chains and up to two nano-silica units (576 atoms each) within a 150 Å periodic box, using the NVT ensemble with a Nosé–Hoover thermostat and a time step of 0.25 fs over a total duration of 42 ps. Experimental analyses via TGA, FTIR, and GC–MS confirmed the formation of key pyrolysis products such as isoprene (C₅H₈), ethylene (C₂H₄), and methane (CH₄). The addition of 60 wt% nano-silica extended the degradation time by approximately 100% and increased the activation energy from 121.9 to 133.8 kJ/mol—a 9.77% rise—suggesting a stabilizing role in the thermal degradation process. Mechanistic insights revealed that degradation proceeds via radical-driven scission near double bonds, with nano-silica modulating both the rate and pathway of decomposition. Overall, the results demonstrate a concentration-dependent dual role of nano-silica in thermal degradation and provide a predictive framework for designing heat-resistant rubber nanocomposites and advancing sustainable pyrolysis-based recycling technologies.

Methods

Cis-1,4-polyisoprene, a naturally derived elastomer with high flexibility and unsaturation, was used as the base polymer (Mw ≈ 38,000 g/mol, Sigma-Aldrich). Nanocomposites containing 30 wt% and 60 wt% nano-silica were prepared via magnetic stirring and compression molding. Pyrolysis experiments were conducted in a lab-scale tubular reactor under nitrogen flow, and thermal behavior was analyzed using thermogravimetric analysis (STA 504, Bahr, Germany). Volatile products and functional groups were identified via FTIR and GC–MS. Reactive molecular dynamics simulations were performed using LAMMPS with a ReaxFF force field parameterized for C/H/O/Si systems. Simulations employed the NVT ensemble with a Nosé–Hoover thermostat to model bond dissociation and reaction pathways at elevated temperatures (1500–2500 K), enabling direct comparison with experimental results.

Context

Spin-crossover (SCO) phenomena in Fe(II) complexes, especially those with octahedral coordination, are of growing interest for their potential in molecular electronics, sensors, and memory devices. These materials exhibit reversible switching between high-spin and low-spin states in response to external stimuli such as temperature or pressure. In this study, we investigate three Fe(II) complexes [Fe(4bt)(_3)](ClO(_4))(_2), [Fe(2bt)(_3)](ClO(_4))(_2).MeOH, and[Fe(3tpH)(_3)](ClO(_4))(_2) to understand their spin-state behavior in relation to both intramolecular and intermolecular interactions. Our computational results indicate that [Fe(2bt)(_3)](ClO(_4))(_2).MeOH and [Fe(3tpH)(_3)](ClO(_4))(_2) undergo spin-crossover transitions with temperature, whereas [Fe(4bt)(_3)](ClO(_4))(_2) stabilizes in the low spin state. Intermolecular interactions such as (pi )-(pi ) stacking and O–H contacts significantly modulate the electronic structure and spin-state energetics. By comparing isolated molecular complexes with their crystalline counterparts, we highlight the critical influence of crystal packing on the SCO mechanism. These insights contribute to the rational design of Fe(II)-based materials with tunable magnetic properties.

Methods

Spin-polarized density functional theory (DFT) calculations were carried out using the Vienna Ab initio Simulation Package (VASP). The Perdew–Burke–Ernzerhof (PBE) functional within the generalized gradient approximation (GGA) was employed, along with Grimme’s D2 dispersion correction to account for van der Waals interactions. The projector augmented wave (PAW) method was used to describe core–valence interactions. Strong correlation effects in Fe 3d orbitals were treated using the PBE+U method.

Context

We theoretically designed and systematically characterized two novel two-dimensional carbon nitride monolayers, h-C(_{10})N(_3) and h-C(_9)N4, based on interconnected acepentalene motifs. Using density functional theory (DFT), we demonstrated their structural stability, confirmed by cohesive energies of (-)6.89 eV/atom and (-)6.92 eV/atom, respectively. Dynamical stability was validated by phonon calculations, revealing no significant imaginary frequencies, while ab initio molecular dynamics simulations showed thermal robustness at 300 K. Both monolayers exhibit metallic behavior, dominated by carbon and nitrogen (p_z) orbitals near the Fermi level. Optical analysis revealed low reflectance and strong absorption peak at 2.2 eV for h-C(_{9})N(_4) and broad absorption within 1.8–3.1 eV for h-C(_{10})N(_3), suggesting potential as visible-light absorbers. Mechanical characterization indicated high elastic stiffness (Young’s modulus, 71-77 N/m), substantial shear resistance (23–25 N/m), and isotropic mechanical behavior (Poisson’s ratio, 0.55). Our findings position these new carbon nitride monolayers as promising candidates for flexible electronic devices, photodetection, and optoelectronic applications.

Methods

First principles were performed using density functional theory (DFT) as implemented in VASP. The PBE functional with PAW pseudopotentials was employed, with a plane-wave cutoff of 520 eV. Thermal stability was assessed by ab initio molecular dynamics (AIMD) simulations at 300 K.

Context

SO_x emissions from diesel fuel necessitate the development of efficient and environmentally friendly desulfurization technologies. This study investigates the catalyst-free photochemical desulfurization mechanism of a diesel model compound, 2-decyl-7-(10-phenyldecyl)dibenzo[b,d]thiophene (D-PD-DBT), under red light irradiation, providing a theoretical foundation for experimentally observed phenomena. Experimental validation confirmed a desulfurization efficiency of up to 46.2%, which was accompanied by the degradation of aromatic structures as observed by FTIR. The proposed three-step mechanism, elucidated computationally, reveals that population of the triplet state (T₁), likely via photosensitization from other chromophores in the diesel matrix, is the critical initiating step. This excited state drastically reduces the HOMO–LUMO gap and chemical hardness, facilitating the initial C–S bond cleavage. The reaction proceeds through the decomposition of intermediates, culminating in the formation of highly stable end products, including benzene, which thermodynamically drives the process to be unidirectional. These findings highlight the fundamental role of excited energy surfaces in enabling C–S bond cleavage without a catalyst.

Methods

All quantum chemical calculations were performed using density functional theory (DFT) at the B3LYP/6-31G level of theory. Excited state analyses were conducted using time-dependent DFT (TD-DFT) to map the photochemical reaction pathway. Reactant, intermediate, and product structures were geometrically optimized and confirmed as minima through harmonic frequency analysis. A set of conceptual DFT reactivity descriptors was calculated from the frontier molecular orbital energies (HOMO and LUMO). The Gaussian 09 software package was used for all computational modeling, with visualization performed using Gaussview and Avogadro.

Context

Quantitative structure–retention relationship is a key method for rapidly predicting the retention time of compounds. However, there have been few systematic studies on the application of QSRR to predict the retention time of phenolic acids, and there are many molecular descriptors and reference standards. This model utilized the experimental chromatographic retention parameter (t_R value) of ten phenolic acid compounds as the dependent variable, while the solubility energy of the compounds in water (E_W) and in methanol (E_M) served as the independent variables. A new QSRR method was established to predict the retention time of phenolic acid compounds, and the QSRR model maintained stable and good prediction performance under a variety of reasonable calculation methods. This study also investigates for the first time the impact of different computational methods under DFT on the accuracy of phenolic acid solvation energy calculations and the predictive performance of QSRR. This research has significantly improved the capability, efficiency, and reliability of theoretical studies, data analysis, and practical applications of phenolic acids throughout our entire field by establishing an optimal computational method. Statistical analysis showed no significant difference in prediction error between models built using the 6-31G basis set and larger, more computationally expensive methods, and the model has been validated to demonstrate good predictive performance. Therefore, using the 6-31G basis set for descriptor calculation is a highly cost-effective choice for phenolic acid QSRR studies.

Methods

HPLC was used to obtain the retention times of the compounds. GaussView 5.0 and Gaussian 09W were employed to perform structural optimization and molecular descriptor calculations for the corresponding compounds using different methods. Stepwise multiple linear regression fitting was then used to calculate the absolute values of the errors in the chromatographic retention parameters for each model. Paired sample t-tests were subsequently conducted to compare the effects of using different methods to calculate solubility on the model performance. The structure optimization was performed employing the DFT-RB3LYP method from the Gaussian09W package, with various calculation settings including 6-31G, 6-31G-D3, 6-31 +  + G-D3, 6-31G*, 6-31G*-D3, 6-31G**, 6-31G**-D3, 6-31 +  + G**-D3, 6-311G, 6-311G-D3, 6-311 +  + G-D3, 6-311G*, 6-311G*-D3, 6-311G**, 6-311G**-D3, and 6-311 +  + G** (a total of 16 different methods) that were calculated. The solubility energies (E_W and E_M) of the phenolic acids in water/methanol were calculated using the DFT-RB3LYP 6-311 +  + G**/methods for structural optimization.