International Journal of Quantum Chemistry最新文献

This study presents an in-depth inquiry into estimating ADME properties for promising anticancer drugs, particularly amino acid-based alkylating agents, through ev-ve degree topological indices and QSPR analysis. The aim of the study is to compare multigraph modeling to simple graph modeling in estimating six ADME properties. Results demonstrate that multigraph modeling's superior performance, with notable high correlations such as $��r�=�0�.�926�$ for maximum passive absorption (MPA) using the M-ev index, compared to simple graph modeling's $��r�=�0�.�68�$ with the $��{�M�}_{�2�}�$-ev index. This emphasizes the need for sophisticated modeling techniques in drug development. The primary objective is to compare multigraph and simple graph modeling using topological structure descriptors, followed by QSPR analysis to determine the better approach in estimating ADME properties. MCDM weight allocation techniques validate correlation values, enhancing understanding of estimators and identifying potential drugs. This underscores the importance of considering various MCDM methods and weight allocation approaches for reliable decision-making in healthcare contexts.

In the present work, we present an investigation of Collins Conjecture, a hypothesis made by D. M. Collins in 1993 relating correlation energy and entanglement entropy, by calculating the ground state and singly-excited triplet-spin 1s2s ³S and 1s3s ³S state energies of the helium iso-electronic sequence, with Z = 2–15. By using extensive orthonormal configuration interaction (CI) type wave functions with B-Spline basis up to about 6000 terms, linear entropy and von Neumann entropy for the abovementioned atomic systems are determined. Together with the Hartree-Fock energies obtained following a self-consistent field theory, we have found that there exist linearly proportionalities between the renormalized correlation energies and entanglement entropies of both linear and von Neumann, showing a support for Collins Conjecture applicable to the ground and singly-excited triplet-spin states in the helium sequence, for a range of finite Z-values.

Exploring suitable catalysts to catalyze the chemical transformation of CO₂ molecules is essential to reduce CO₂ levels. In this article, catalyst models of Fe-C₄, Fe-N₄, and Fe-Si₄ were constructed using the density functional theory (DFT) calculations, and the reaction mechanisms of CO₂ hydrogenation over these three catalysts were calculated and analyzed. The results showed that the doping of N atoms lowered the energy barrier of the second hydrogenation step compared with that of Fe-C₄ catalyst, while the doping of Si atoms changed the electron distribution on the surface of the catalyst and formed new Si adsorption sites. And the Fe-Si₄ catalyst had a stronger ability to activate CO₂ molecules as well as stronger catalytic performance compared with the Fe-C₄ and Fe-N₄ catalysts, which was mainly attributed to the synergistic catalytic effect between the doped Si atoms and the Fe metal atom.

Flavonoids are known for its mechanism of antioxidant property which can prevent DNA damage. These natural products' anticancer and antiviral properties are linked to their mechanism of action. Various examinations reveals that, there will be a adjacent relationship between the molecular structure and their physicochemical properties such as boiling point, melting point, entropy, and so forth. In this work we have presented the physicochemical analysis and determination of $��\pi �$-electron energy for some bio-molecular structures namely Erythroxylum Flavonoids, DNA (deoxyribonuclic acid), and RNA (ribonuclic acid). Additionally, the QSPR analysis of bio-molecular structures using ten degree based topological indices are discussed to assist researchers in understanding the chemical reactions and physical properties related with them. Furthermore, we demonstrated that the indices values correlate well with the physicochemical properties of flavonoids, DNA, and RNA molecular structures from the presented regression model.

Lead halide perovskites have been replaced by the environmentally acceptable and effective lead-free double perovskite material. Double perovskites are innovative compounds for sustainable energy and budding substitutes to organic as well as lead-based solar cells. In the current study, it has been expounded on the structural, electronic, thermoelectric, as well as thermodynamic characteristics of newly designed double perovskites In₂TiX₆ (X = Br, I) by means of ab-initio computations relied on the FP-LAPW tactics and semi-classical Boltzmann transport theory with PBE-GGA as exchange correlation potential. To obtain accurate value of band gaps (1.294 eV and 1.025 eV), TB-mBJ approximation has been used along with PBE-GGA. The best combinations of both compounds have spectroscopic limited maximum efficiency (SLME) values 33.96% and 31.63%, that are appropriate for solar cell absorbers, at 300 K, respectively. We have also computed Debye temperature $��\left({\theta }_D\right)�$ and Grüneisen parameter $��\left(\gamma \right)�$ to find the lattice thermal conductivity for both the investigated alloys. Thermoelectric properties have been labeled by Seebeck coefficient, electrical as well as thermal conductivities, and figure of merit. The peak values of Seebeck coefficient of 248 μV/K and 202 μV/K are observed for In₂TiBr₆ and In₂TiI₆ respectively in the p-type regions. Attained results illustrates that the investigated In₂TiX₆ may be contender in thermoelectric due to their high figure of merit in low as moderate temperatures. Our results suggest that these materials are viable for use in thermoelectric devices.

Using ab-initio DFT based simulations, the hydrogen storage capacity of transition metal (TM = Ni, Pd) decorated doped germanium carbide nanotubes (GeCNTs) with heteroatoms (B, N, Ga, and As) has been examined. The study reveals that each Ni atom bonded on GeCB, GeCN, GeCGa, and GeCAs can attach at the most of 3H₂, 2H₂, 4H₂, and 5H₂ molecules with an average binding energy of −.36, −.40, −.32, and −.37 eV/H₂, respectively. When doped GeCNTs are fully decorated with Ni atoms, their gravimetric hydrogen storage capacities are around 4.05, 2.73, 5.38, and 6.57 wt%, respectively. The desorption temperature of the systems is 460, 511, 404, and 473 K, respectively. When doped GeCNTs are fully decorated with Pd atoms, their gravimetric hydrogen storage capacities are around 2.07, 3.07, 4.05, and 3.07 wt%, respectively. These findings demonstrate that doped-GeC adorned with Pd does not satisfy US DOE hydrogen storage requirements. The molecular dynamic (MD) calculations are utilized to examine the stability of the considered structures. The results demonstrate that Ni-adorned GeCAs are a suitable material for hydrogen storage, which will motivate scientists to fabricate GeCAs-based fuel cell devices.

The relativistic effect enhances spin-orbit coupling (SOC), making metal complexes potential candidates for phosphorescent OLED emitters. However, the relativistic effect profoundly influences the donor and acceptor interactions (D-A), resulting in unique electron transition processes. By stabilizing the s orbitals and destabilizing the d orbitals of the center metal atom, the relativistic effect enhances donation, back donation, and the trans effect in PtN1N more than in PdN1N. Particularly, the back donation in PtN1N is approximately four times greater than that in PdN1N, contributing to the greater stability and rigidity in PtN1N. Furthermore, the relativistic effect enhances the SOC and reduces the excitation energy and stabilizes the excited states of PtN1N. Consequently, the radiative decay rate $��k_p�$ and non-radiative rate $��k_{\mathrm{nr}}�$ are accelerated simultaneously. The reverse intersystem crossing rate $��k_{\text{RISC}}�\left(�T_3�\to �S_1�\right)�$ in PdN1N is accelerated by high temperature, which is responsible for thermally activated delayed fluorescence (TADF).

Ab-initio molecular dynamics (AIMD) is a key method for realistic simulation of complex atomistic systems and processes in nanoscale. In AIMD, finite-temperature dynamical trajectories are generated by using forces computed from electronic structure calculations. In systems with high numbers of components a typical AIMD run is computationally demanding. On the other hand, machine learning (ML) is a subfield of the artificial intelligence that consist in a set of algorithms that show learning by experience with the use of input and output data where algorithms are capable of analysing and predicting the future. At present, the main application of ML techniques in atomic simulations is the development of new interatomic potentials to correctly describe the potential energy surfaces (PES). This technique is in constant progress since its inception around 30 years ago. The ML potentials combine the advantages of classical and Ab-initio methods, that is, the efficiency of a simple functional form and the accuracy of first principles calculations. In this article we review the evolution of four generations of machine learning potentials and some of their most notable applications. This review focuses on MLPs based on neural networks. Also, we present a state of art of this topic and future trends. Finally, we report the results of a scientometric study (covering the period 1995–2023) about the impact of ML techniques applied to atomistic simulations, distribution of publications by geographical regions and hot topics investigated in the literature.