Journal of Computational Chemistry最新文献

Tubular g-C₃N₄ nanotubes (CNNTs), experimentally realizable counterparts of 2D g-C₃N₄, present promising opportunities for tuning electronic, optical, and mechanical properties, which remain largely underexplored. In this work, density functional theory calculations are utilized to evaluate the band gaps and elastic moduli of CNNTs with varying diameters and chiralities under applied strain. The results reveal that the Young's moduli of these CNNTs span from 40 to 230 GPa, while the band gaps show an inverse correlation with the aspect ratio. This systematic study advances the fundamental understanding of CNNTs and provides valuable insights for the rational design of nanostructured materials aimed at applications including photocatalysis and electrocatalysis.

The hetero coalescences of the Cu₁₄₇ and Ag₁₄₇ clusters at room temperature and 1300 K have been simulated by molecular dynamics approaches within embedded atom method framework, and then the structural transformation of alloying clusters formed after the coalescence is investigated during heating and cooling. The activation energy is used to describe the potential barriers that need to be overcome to carry out the merger, the potential energy characterizes the structural transition the free energy difference reflects the driving force for the coalescence of the clusters. The simulation results show that different contacting modes and different distances between the two clusters at 300 K affect the heterogeneously coalescing processes and the alloying clusters formed, and that the increase in temperature greatly affects the motion of the atoms as well as the atomic packing structures within the alloying clusters. At the beginning of coalescence, the two clusters are in contact with each other, forming a connecting neck, and the Ag atoms diffuse along the surface of the Cu clusters. With the increase of temperature, Ag atoms gradually wrap around the Cu clusters, and the Cu clusters also undergo structural changes at high temperatures, and the Cu-Ag alloy clusters with different initial cohesive distances and cohesive orientations show different packing patterns. For the Cu-Ag clusters obtained via high-temperature coalescence, the cooling stage not only exhibits distinctly different structural transformation processes, but the structural transformation temperatures corresponding to each process also show significant differences.

Core-shell (CS) particle consisting of Pt shell and base metal core is one of promising candidates of excellent electrode catalyst for fuel cell, because it is highly active and highly stable like Pt particle but less expensive than Pt particle. However, its stability has been unclear. Herein, icosahedral M₁₃@Pt₄₂ (M = 3d transition metals, Sc to Cu) CS particles consisting of Pt₄₂ shell and M₁₃ core are systematically investigated using DFT calculations. The CS structures of Co₁₃@Pt₄₂, Ni₁₃@Pt₄₂, and Cu₁₃@Pt₄₂ are calculated to be more stable than their non-core-shell (NCS) structures in which one 3d metal atom of the M₁₃ core is exchanged with one Pt atom of the Pt₄₂ shell. For Sc₁₃Pt₄₂, Ti₁₃Pt₄₂, V₁₃Pt₄₂, Cr₁₃Pt₄₂, Mn₁₃Pt₄₂, and Fe₁₃Pt₄₂, on the other hand, the NCS structure is more stable than the CS one. Small deformation energy of the Pt₄₂ shell compared to that of the Pt₄₁M shell and small stabilization energy by Pt–M exchange between the Pt₄₂ shell and the M₁₃ core are particularly important for stabilizing the CS structure. Late 3d metal elements in the first transition series of the periodic table such as Co, Ni, and Cu are suitable for producing a stable M₁₃@Pt₄₂ CS particle because their electronegativities are larger than those of the early and middle 3d transition metal elements and their atomic sizes are smaller than those of the early 3d metal elements such as Sc and Ti atoms. O₂ adsorption to M₁₃@Pt₄₂ (M = Co, Ni, and Cu) CS particle occurs at the edge Pt atom and the vertex Pt atom in a side-on manner. The adsorption energies of O₂ molecule to Co₁₃@Pt₄₂, Ni₁₃@Pt₄₂, and Cu₁₃@Pt₄₂ CS particles are smaller than that to Pt₅₅ particle, indicating that the M₁₃ core decreases the reactivity of the Pt₄₂ shell for O₂ adsorption.

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.