Journal of Computational Chemistry最新文献

LiF-SmF₃ systems are the basic medium for the electrolytic preparation of Sm-Fe alloys by molten salt electrolysis. An in-depth analysis of its properties and local structural features is of great theoretical and practical significance for optimizing the electrolytic preparation process of Sm-Fe alloys. Owing to the long lead time and high cost of systematic measurements of the properties of high-temperature fluoride molten salts, machine learning-based calculations have become an efficient way to obtain the properties and structural features of molten salt systems. In this study, a machine learning potential model for the analysis of LiF-SmF₃ systems is developed. Its computational accuracy can reach 96.92%, with root-mean-square deviations of 5.48 × 10⁻³ eV/atom and 6.23 × 10⁻² eV/Å for energy and force, respectively, indicating an accuracy comparable to that of density functional calculations. The machine learning potential model was used to perform molecular dynamics simulations of the LiF-SmF₃ system in the temperature range of 1100–1350 K. The density and viscosity of the system were calculated, and their deviations from the experimental measurements ranged from about 1.03%–2.77% and 3.36%–4.58%, respectively. The ion self-diffusion coefficients of the system were calculated, and the structural features of the system were analyzed using the radial distribution function of the ion pairs.

Hydrogenase enzymes play a crucial role in generating energy for microorganisms by catalyzing the reversible oxidation of molecular hydrogen to protons. This catalytic mechanism has been well studied using computational models of varying complexity, ranging from smaller QM-cluster models of the enzyme active site to QM/MM models that capture the full enzyme structure. However, differences among studies have produced conflicting predictions for the energetics of certain reaction steps. This work focuses on characterizing one step—a cysteine–histidine proton transfer of Desulfovibrio fructosovorans [Ni, Fe]-hydrogenase—using a series of QM-cluster models to explore how model design influences predicted reaction thermodynamics. The Residue Interaction Network-based ResidUe Selector (RINRUS) toolkit was used to systematically create QM-cluster models based on either inter-residue distances or contact metrics from the active site [Ni, Fe] cluster. It is shown that QM-cluster models can achieve reaction energy predictions comparable to QM/MM and “big-QM” models when active site models are designed based on inter-residue contact interactions and with careful consideration of charged residues. Distance-based residue selection, a common strategy for QM-cluster model design, is not as effective compared to the RINRUS rules-based residue ranking approach from inter-residue contact counts. Large differences between previously reported QM and QM/MM reaction energies are resolved with RINRUS-based models, even at a modest level of electronic structure theory (B3LYP with modified LANL2DZ(d) basis sets/effective core potentials on metal atoms and 6-31G(d′)/6-31G on nonmetal atoms). Overall, this [Ni, Fe]-hydrogenase case study underscores the need for careful model design when studying complex biological systems and demonstrates how RINRUS can provide a framework towards addressing this challenge.

The bonding in transition metal carbonyls is discussed through the Dewar-Chatt-Duncanson (DCD) model of σ-donation from the ligand and π-back donation from the metal. However, there are no reports of direct quantification of the donation and back donation. Whenever it comes to the aspect of electron transfer, the fundamental concepts that are important are ionization energy (I), electron affinity (A), electronegativity (χ), hardness (η), and electrophilicity (ω). The global reactivity indices are calculated using conceptual density functional theory (CDFT). It was found that the back bonding and hence the experimental CO stretching frequency provide excellent correlation with I, A, and χ with r² values of 0.963, 0.903, and 0.965, respectively. While in correlation to η, two categories are developed in correlation to ν_CO. However, the best correlation is achieved from the local electrophilicity description of the multiphilic descriptor (Δω_M). Finally, the directional approach of the back donation is tackled by the extended transition state-natural orbitals for chemical valence (ETS-NOCV) method, considering CO as one fragment and the rest as the other. A very good correlation to ν_CO is found with r² = 0.964. The back-bonding aspect is also explained from the second-order perturbation energy term as obtained from the natural bond orbital analysis. These correlations remain valid upon changing the functional and basis sets. In addition, considering Sc(CO) as the starting complex, hydrogen molecules are added to obtain Sc(CO)(H₂)_n (n = 1–5) complexes. In these complexes, the Kubas-type interactions are studied employing ETS-NOCV and quantum theory of atoms in molecules (QTAIM) analyses.

The development of advanced materials for the detection and safe handling of energetic compounds such as TATB (1,3,5-triamino-2,4,6-trinitrobenzene) and TNT (2,4,6-trinitrotoluene) is critical for defense, homeland security, and industrial safety. However, current technologies often suffer from limited cost-efficiency, sensitivity, and real-world applicability. While traditional carbon allotropes such as graphene, fullerenes, and carbon nanotubes have been explored for explosive sensing and hazard mitigation, emerging sp-hybridized carbon nanostructures like cyclo[n]carbons remain underexplored. In this article, we present a theoretical investigation of cyclo[16]carbon (C₁₆), a novel sp-hybridized carbon ring, for interaction with energetic molecules. TNT was selected as a benchmark explosive due to its widespread use, whereas TATB was chosen for its remarkable insensitivity, allowing us to explore safe handling and adsorption scenarios. Our results reveal the formation of stable hollow-layered and sandwich-type supramolecular complexes with TNT and TATB via non-covalent C…O, C…N, and C…C interactions. Notably, the C₁₆–TNT and C₁₆–TATB complexes exhibit enhanced thermodynamic stability and reduced electrostatic sensitivity. Binding energy and electronic structure analyses indicate tunable optical properties, supporting the role of C₁₆ as a metal-free, spectroscopically active sensor. These findings underscore the dual functionality of cyclo[16]carbon in promoting safe handling and detection of high-energy materials, positioning it as a promising platform for passive sensing and hazard mitigation in challenging environments.