Journal of Molecular Modeling最新文献

Context

The C–C bonds in alkanes are generally considered nonpolar covalent bonds, but the mechanism by which fluorine substitution affects the polarity and cleavage mode of C–C bonds in alkanes remains unclear. In this study, using 1,1,1-trifluoroethane (CH₃-CF₃) as a model, we systematically investigated the reconstruction mechanism of the electronic structure of C–C bonds induced by fluorine substitution through density functional theory calculations, combined with bond critical point (BCP) topological analysis, atomic charge calculation, and flexible scan simulation. The results demonstrate that fluorine substitution transforms the C–C bond into a polar covalent bond via a strong electron-withdrawing inductive effect, leading to the separation of positive and negative charges on the two carbon atoms and breaking the nonpolar symmetric distribution. Flexible scan simulations reveal that the C–C bond cleavage in CH₃-CH₃ follows a typical covalent bond homolysis, whereas that in CH₃-CF₃ exhibits ionic bond characteristics. This study uncovers the reconstruction mechanism of C–C bonds from nonpolar to polar induced by fluorine substitution and its disruptive impact on the bond cleavage pathway, providing a theoretical basis for understanding the structure–property relationships of complex fluoroalkanes.

Methods

All quantum chemical calculations adopted the M06-2X functional paired with the Def2-TZVP basis set. Initial molecular structures were built in GaussView, with geometry optimization performed via Gaussian 16 to obtain optimized configurations and wavefunction files. Wavefunction analyses (including BCP topology, Hirshfeld atomic charge, Mayer bond order, and spin population/density calculations) were conducted using Multiwfn.

Graphic Abstract

Context

This study used the PTRA-based molecule to design and investigate seven new donor-acceptor1-π-acceptor2 (D-A1-π-A2) organic dyes (PTRA1-PTRA7) for dye-sensitized solar cells (DSSCs). All dyes had an electron-A2 group made of rhodanine-3-acetic acid and a thiophene group as a spacer, while the electron-A1 unit varied from D-A1-π-A2. Optoelectronics was explored in relation to a structure and its influences. For the dyes PTRA1–PTRA7, the computational analysis of density functional theory (DFT) and its extended time-dependent DFT (TD-DFT) approaches was performed. The driving force of electron injection ((Delta G_{inject})), dye regeneration ((Delta G_{reg})), exciton binding energy (E_b), molecular orbitals (MOs) energy levels, optical ultraviolet-visible (UV-Vis) spectra, and electronic properties were all thoroughly discussed. The findings show that PTRA1–PTRA7 have smaller energy gaps (E_g) and higher absorption wavelength (λ_max) than PTRA. Out of all of them, PTRA6 has the lowest E_g (2.15 eV) and the red-shifted λ_max (559 nm). It has been determined that dyes PTRA1–PTRA7 are the most promising option for having highly efficient DSSCs, especially PTRA6. Additionally, these molecules serve as the most promising functional group in D-A1-π-A2 dyes for auxiliary electron-A. Due to its excellent electronic, optical, and photovoltaic (PV) characteristics, it could be utilized as a potential sensitizer for DSSCs.

Methods

Formalisms of the conductor-like polarizable continuum model (CPCM) have been used to study solvent effects. The results of the CPCM/TD-DFT demonstrate that precise absorption energies can only be obtained when the solvent effect is taken into account in the geometries of the excited states. The Gaussian 09w software package is used for related calculations.

Context

The Al₂Cu precipitated phase plays a critical role in governing the stability and mechanical properties of age-hardened Al–Cu alloys, which are widely used in the aerospace and automotive industries due to their high strength-to-weight ratio. However, the influence of alloying elements (e.g., Fe, Mn, Mg, Sc, and Zr) on the interfacial stability and nucleation behavior of Al₂Cu remains insufficiently understood, limiting the rational design of advanced Al–Cu alloys with optimized performance. This study addresses this gap by investigating how elemental segregation affects the structural stability and nucleation thermodynamics of Al₂Cu interfaces, offering insights into strategies for enhancing the mechanical properties and thermal stability of these alloys.

Methods

First-principles calculations based on density functional theory (DFT) were employed to evaluate the effects of segregated elements (Fe, Mn, Mg, Sc, and Zr) on the coherent strain energy and interface energy of Al/Al₂Cu interfaces. Electronic structure calculations were performed using the Vienna ab-initio simulation package (VASP), employing the Perdew–Burke–Ernzerhof (PBE) functional within the generalized gradient approximation (GGA) to account for exchange–correlation effects. To further elucidate the bonding mechanisms, interface stability was analyzed through detailed electronic structure investigations. Classical nucleation theory was applied to predict phase nucleation thermodynamics at aging temperatures, examining the formation of core–shell precipitates (Al₂Cu cores with solute-rich shells) as a function of precipitate size.

Context

The presence of potentially carcinogenic nitrosamines in drugs has been a worldwide concern, driving strategies to control or mitigate their formation to protect patient health. Understanding the critical factors for N-nitrosation, such as mechanisms and energy barriers, enhances the risk assessment process to understand potential nitrosamine formation. Evaluation of the structural impact of amines on the N-nitrosation rate in the presence of nitrites and acidic media is of great interest to pharmaceutical companies assessing the risk of nitrosamine drug substance–related impurities. A range of secondary amines was explored using DFT calculations to assess the impact of electronic and steric effects on activation energy. Asym-N₂O₃ was selected as the nitrosating agent since its reaction was shown to be favorable following screening of pathways employing nitrosyl chloride, nitrous acid, asym-N₂O₃, sym-N₂O₃, and trans-cis-N₂O₃. The relatively low activation energies obtained for all amines indicate the reaction is very likely to occur if the reactive components encounter, even for amines with sterically hindered and electron-withdrawing groups. Understanding the interaction between the amine and nitrosating agent is therefore the defining factor in the risk of formation of more complex nitrosamines within drugs.

Methods

Calculations were performed using the Gaussian-16 program. The B3LYP-D3/def2-TZVP level of theory was applied for structure optimizations. The IEF-PCM implicit model was used for the solvent effect. Intrinsic reaction coordinate calculations were carried out to connect the transition state with the associated minimum.

Graphical Abstract

Context

Covalent Organic Frameworks (COFs), which are frameworks composed of light atoms held together by strong covalent bonds, are generating interest as potential materials for applications such as renewable energy and gas capture. We employed Density Functional Theory (DFT) calculations, as implemented in the VASP code, to look at both 2D and 3D COFs. We systematically analyzed various properties, including structural stability, phonon dispersion, electronic structures, density of states, adsorption behavior, and mechanical properties. To get better accuracy, we took into account van der Waals interactions and even used hybrid functionals. What we found was that 3D COFs generally exhibit greater mechanical strength and, in most cases, better gas adsorption, which seems to come from their interconnected pore structures. On the other hand, 2D COFs exhibit enhanced π-electron delocalization and direct band gaps of approximately 2.5 eV, which may be helpful in sensors and optoelectronics. Phonon analyses verified the dynamical stability of both structures. Ultimately, these results underscore the importance of dimensionality in tailoring COF properties for energy and electronic applications.

Method

First-principles simulations were performed using Density Functional Theory (DFT) within the Vienna Ab initio Simulation Package (VASP). To account for exchange–correlation effects, we employed the Generalised Gradient Approximation (GGA) in the Perdew–Burke–Ernzerhof (PBE) formulation, and we also utilised projector-augmented wave (PAW) pseudopotentials. Hybrid functional (HSE06) and DFT-D3 van der Waals corrections were introduced to improve the accuracy of our band gap prediction. The plane-wave cutoffs were set at 500 eV for the calculations, and Monkhorst–Pack k-point meshes were used with a 3 × 3 × 1 (2D) and 2 × 2 × 2 (3D) grid. Evaluated were structural optimisations, band structures, total and projected DOS, adsorption energies, and charge transfer (using Bader analysis). Assessment of bonding features utilised the Electron Localisation Function (ELF) and charge density difference (Δρ) visualisations. The Phonopy package was used to calculate Phonon dispersions and thus confirm the dynamic stability of the COFs.

Context

The biological activity of steroidal compounds such as dexamethasone (DX) and hydrocortisone (HC) is closely linked to subtle variations in their molecular structure and electronic properties. This study provides a comparative quantum chemical analysis of DX and HC to clarify how these differences influence hydrogen bonding strength, reactivity, and their potential interactions with the glucocorticoid receptor (GR). Optimized geometries, natural bond orbital (NBO) analyses, frontier molecular orbitals (FMO), global reactivity descriptors, and average local ionization energy (ALIE) calculations demonstrate that DX exhibits greater polarity and electrophilic character compared to HC. These differences help explain the stronger receptor binding affinity observed for DX. Indeed, notably, despite the inherent limitations of gas-phase DFT calculations compared to experimental X-ray data, the theoretical results exhibit good agreement with experimental observations, suggesting the reliability of the computational approach in predicting molecular interactions within the GR active site.

Methods

All quantum chemical calculations were performed using density functional theory (DFT) with the B3LYP functional and 6-311++G(d,p) basis set. Structural optimization, FMO analysis, global reactivity descriptors, and dipole moment evaluations were carried out in Gaussian 09. NBO analysis was performed with NBO 5.0. Average local ionization energy (ALIE) surfaces were generated using Multiwfn 3.8, and molecular visualizations were produced with GaussView 5.0.

Context

This study investigates the antioxidant behavior and chelating properties of thirteen phenolic compounds identified in Carapa guianensis agro-industrial waste. Using quantum mechanical methods, we analyzed the molecular structure and solvent effects, revealing potential applications in the food and pharmaceutical industries. Approximately 85% of these compounds demonstrated superior antioxidant performance compared to the phenol backbone, with three compounds rivaling quercetin and ascorbic acid in all tested environments. Solvent polarity significantly influenced the antioxidant mechanism: while hydrogen atom transfer (HAT) dominated in the gas phase, sequential proton loss electron transfer (SPLET) became prevalent in polar solvents. Hydrogen abstraction occurred primarily at the meta position, though polar solvents increased activity at the para site. These predictions are confirmed by simulating the chemical reactions between the aromatic compounds and free OH radicals. The analysis of the hydrogen abstraction reactions indicates that the inclusion of Hartree-Fock exchange-correlation and dispersion corrections is essential to describe hydrogen abstraction by explicitly free radicals. These findings not only underscore the commercial potential of Carapa guianensis waste but also provide a comprehensive understanding of its antioxidant mechanisms, contributing to the sustainable use of natural resources for health and environmental benefits.

Methods

This is a two-step methodology. First, we select the best antioxidant molecules through thermochemical analysis. Then, we explore the hydrogen scavenging mechanism using transition state theory. All quantum mechanics and thermodynamic analyses were carried out within the framework of the density functional theory using the M06-2X, (omega )B97, (omega )B97XD, and CAM-B3LYP variants associated with the Pople’s 6-311++G(d, p) basis set. The effects of the solvent are also systematically investigated by considering the Solvent Density Model for different polar and nonpolar environments. Naturally, the unrestricted wave-function formalism is accounted for once the thermodynamic description of the antioxidant mechanisms involves optimizing open-shell structures. Additionally, the chemical reaction of the hydrogen abstraction due to OH radicals is ensured by applying the Synchronous Transit-Guided Quasi-Newton Method, which allows us to estimate the transition state structures and the energy barrier of the reaction. At this stage, DFT methods like CAM-B3LYP and (omega )B97XD were applied in association with the 6-31+G(d). All calculations were carried out taking advantage of the Gaussian 16 program.

Context

Organic solar cells (OSCs) offer a promising route toward flexible and sustainable energy technologies, yet predictive modeling of device parameters remains challenging due to the chemical diversity of donor–acceptor systems and morphology-dependent effects. In this work, we present the first systematic demonstration of using autoencoder-compressed molecular fingerprints with tree-based machine learning models to predict key OSC performance metrics—power conversion efficiency (PCE), open-circuit voltage (V_oc), short-circuit current (J_sc), and fill factor (FF)—from a broad experimental dataset of  2500 donor–acceptor pairs, including both fullerene and non-fullerene acceptors. These compact models, trained on compressed descriptors of only 32 dimensions, achieved strong predictive accuracy (Pearson (r > 0.95), (MAE < 0.4), (RMSE < 0.95)) while remaining lightweight enough to run on standard computing hardware. As a complementary result, some k-nearest neighbor models achieved near-perfect correlations ((r sim 0.99)) and quite small errors ((MAE < 0.044) and (RMSE<0.4)) in general, demonstrating the surprising strength of simple, instance-based learners when sufficient descriptive features are available. Supporting analyses reveal that fullerene datasets are more easily modeled than chemically diverse non-fullerene sets, that fingerprints encode substantial structural information, and that kernel density analyses identify critical ranges of molecular weight and energy offsets for high-efficiency devices. Collectively, this study establishes compressed fingerprint descriptors as a powerful, computationally inexpensive foundation for predictive modeling in OSCs, while also showcasing the unexpected efficacy of k-NN models trained on conventional descriptors. Together, these approaches provide a scalable path toward high-throughput prediction and guided molecular design of next-generation organic photovoltaic materials.

Methods

The dataset used in this work comprises approximately 2500 experimentally characterized donor–acceptor pairs from bulk heterojunction OSCs. These include both fullerene and non-fullerene acceptor systems. For each pair, the database provides electronic descriptors, polymerization-related metrics, and the SMILES representations of the donor and acceptor molecules. Molecular fingerprints were computed from SMILES codes using the RDKit and CDK cheminformatics toolkits. A variety of machine learning models were explored, including feedforward neural networks, autoencoders for feature compression, tree-based ensemble methods, and kernel-based regression algorithms. Hyperparameter tuning was carried out using the Optuna and BayesSearchCV libraries to ensure optimal model performance.

Context

Intramolecular proton transfer (IPT) has been investigated in 8-hydroxyquinoline (8-HQ) and one of its derivatives, 5,6-dihydroquinolin-8-ol (8-DQ). Computations identify the global minima as the enol (E) in S₀ and the proton transferred keto (K^*) form in S₁. The barrier for IPT process reduces on excitation.The pathway was investigated by altering IPT coordinate and intrinsic reaction coordinates (IRC). The frontier molecular orbital analysis shows the shift in electron distribution to the pyridine ring upon excitation. The OH stretching vibration shows a red shift upon E → E^* excitation, confirming the possibility of excited state intramolecular proton transfer (ESIPT). These molecules adhere to the maximum hardness principle (MHP) and the minimum electrophilicity principle (MEP). Natural bond orbital (NBO) analysis reveals the onset of hyperconjugative interactions involving the lone pair of N atom in S₁. Ring and bond critical points are identified. A mixed covalent and noncovalent character of the N₁···H₁₂ bonds signifies the onset of ESIPT. DFT computations with M06-2X and B3LYP functionals show some marked differences, particularly in terms of hydrogen bond lengths and charges, demanding experimental results to ascertain the choice of functionals.

Methods

DFT (B3LYP, M06-2X, and ωB97X-D functionals) and ab initio methods (MP2) are used in S₀ whereas CIS and TDDFT in S₁. 6–311 +  + G(d, p) and aug-cc-pVDZ are the corresponding basis sets used. The computations involve the Gaussian 09 program, which include NBO analysis. PED and QTAIM analyses are performed through the Veda 4 and Multiwfn programs.