International Journal of Quantum Chemistry最新文献

The advancement of magnesium ion batteries necessitates the exploration of novel high-capacity anode materials. This research examines the viability of HfSe₂ monolayers as a potential anode material for magnesium ion batteries, utilizing first-principles calculations. The findings indicate that HfSe₂ demonstrates substantial electrical conductivity as an electrode material, with its electronic conductivity remaining unaffected by applied strain. Additionally, a low diffusion barrier of 0.071 eV contributes to its high rate performance. Notably, HfSe₂ possesses a significant theoretical capacity of 480.735 mAh/g, accompanied by a relatively low open circuit voltage of 0.203 V. These results provide insights into the magnesium storage mechanism of HfSe₂ monolayers and inform the design of magnesium ion batteries.

The properties of the output probe field in a qubit-embedded system containing two coupled cavities are investigated theoretically. A strong pump field and a weak probe field drive the left cavity. The right cavity couples the mechanical oscillator through the photothermal effect. Simultaneously, a qubit couples the right cavity and the mechanical oscillator through Jaynes-Cummings coupling. Coupled-resonator induced transparency (CRIT) is realized in the bare coupled cavities firstly. When considering the photothermal effect, photothermally induced transparency (PTIT) can also be achieved. Further, when a qubit is inserted, the qubit-assisted induced transparency (QIT) is demonstrated. Tunable optical response, along with slow-fast light at different frequencies, is shown by adjusting the system coupling strengths and frequency detunings. Especially, we demonstrate the group delay in the PTIT can also be manipulated by the photon tunneling between the double cavities.

Quaternary Heusler LiTiRhZ (Z = Si, Ge, Sn) compounds are investigated for mechanical and thermodynamic stability and their suitability as potential thermoelectric materials in a high-temperature range. The density functional theory and Boltzmann transport equations have been used for the calculations of structural, electronic, phonon dynamics, elastic, and thermoelectric properties. The compounds exhibit indirect band gaps of 1.076, 1.132, and 1.032 eV in LiTiRhZ (Z = Si, Ge, Sn), respectively, confirming their semiconducting nature. The negative formation energies and high melting points (~1800 K) suggest structural stability and experimental feasibility. Elastic and phonon calculations confirm mechanical and dynamical stability, along with ductile and anisotropic behavior. For a better understanding of thermodynamic properties, free energy, entropy, and specific heat at constant volume are also investigated up to 1000 K temperature. We obtained the increasing nature of power factor in all studied compounds, indicating the high value of figure of merit (ZT), particularly in the high-temperature region, with LiTiRhSi achieving a maximum ZT ~ 0.69 at 1000 K, showing its potential for high-temperature thermoelectric applications. The higher and stable values of ZT as compared to the other reports in the high-temperature range may provide strong support for experimental research on the studied compounds.

In this article, we compute exponential topological indices of benzenoid hydrocarbons using MATLAB based on their edge connectivity and investigate their correlation with several thermodynamic properties, including boiling point, entropy, enthalpy of formation, Kovats retention index, octanol–water partition coefficient, and total $��\pi �$-electron energy. We develop both linear and quadratic models to predict these properties and demonstrate that the indices exhibit strong correlations with them. Notably, the correlation coefficients between most indices and the total $��\pi �$-electron energy exceed 0.98. These results suggest that exponential topological indices are effective predictors of thermodynamic properties in polycyclic aromatic compounds and may aid future research on benzenoid hydrocarbons.

Bleomycin (BLM) is the first-line clinical antibiotic used in the treatment of cancer. It inhibits DNA metabolism and is used in conjunction with other anticancer medications to treat various kinds of malignant tumors. This work focuses on examining more fully the bioactivity of BLM as both anticancer and antibacterial agents. Due to the structure–function relationship, the conformational study of the molecule was carried out first, and its potential conformations were identified. Afterwards, using the energy minimization feature of the YASARA structure program, the obtained lowest energy conformation of the molecule and the receptor taken from the protein databank (ligand-free) were optimized. BLM was subjected to molecular docking tests with two antibiotic-binding proteins (PDB IDs: 1ewj and 2zw7) to determine its action mechanism as a TN5 transposon inhibitor. Moreover, its binding affinities towards thymidylate kinase (TMK) (PDB ID: 4qgg) Escherichia coli DNA gyrase B (PDB ID: 6f86) were also evaluated to reveal its antibacterial potential. Additionally, ligand-receptor interactions were assessed via molecular dynamics (MD) process to confirm the stability of BLM docked into antibiotic binding protein (1ewj), TMK (4qgg) and E. coli DNA gyrase B (6f86) within 500 ns (for 1ewj and 6f86) or 200 ns of time (for 4qgg). Molecular mechanics/Poisson-Boltzmann Surface Area methods (MM/PBSA) were used to compute the binding energies through MD simulations. Dynamics cross correlation matrices (DCCM) analysis and principal component analysis (PCA) on the MD data were performed. Results have cleared the mechanism of action of BLM having anticancer and antibacterial properties.

The exact solution of strongly correlated systems is a computationally demanding problem, scaling exponentially with system size. Over the decades, several approximate methods have been developed to address this scaling problem. Among these, matrix product state (MPS) has emerged as physically intuitive and polynomially scaling. Methods such as density matrix renormalization group (DMRG), time evolution block decimation (TEBD), etc., use the underlying MPS wavefunction ansatz to optimize it variationally. MPS can also be utilized as a machine learning model to select the most important electronic configurations, as employed in selected configuration interactions. This work uses machine learning (ML) optimization of MPS wavefunctions to eliminate the need for multiple diagonalizations during the optimization process. We compare the performance of the machine-learned MPS-assisted selected configuration interaction approach, often mentioned as a classifier, to the machine-learned MPS wavefunction ansatz itself. While showing the viability of the machine-learned MPS ansatz, it is observed that within ML, MPS is best used as a selected configuration interaction.

Developing efficient electrocatalysts for the electrochemical reduction of CO₂ to valuable chemicals is crucial for reducing CO₂ concentration and achieving carbon neutrality. This study focuses on the nitrogen-carbon monolayer (CN) and systematically investigates the electrocatalytic CO₂ reduction reaction (CO₂RR) by introducing boron (B) as a central metal-coordination atom. We constructed 26 TM@B-CN catalysts and identified Ta@B-CN as exhibiting superior CO₂RR activity. Systematic studies revealed that Ta@B-CN not only possesses higher Faradaic Efficiency (FE) but also exhibits lower potential limitations for CO₂ reduction to CO. Compared with the computational Hydrogen Electrode (CHE), the limiting potential for the CO₂ reduction to CO on Ta@B-CN is only about −0.13 V. This research elucidates the intrinsic mechanisms of excellent catalysts through in-depth computational and electronic structure analysis. The findings provide valuable guidance for designing more efficient CO₂RR catalysts, holding significant implications for addressing climate change and advancing clean energy development.