Journal of Computational Chemistry最新文献

Toxic gases monitoring and detection are fundamental to lessening public health problems. Therefore, in this work, to explore emergent sensor materials, 3d-metal dimers-doped hexagonal boron nitride (h-BN) structures were investigated employing auxiliary density functional theory (ADFT) as novel CO and NO gas sensors. Firstly, the stabilities of Co₂, Ni₂, and Cu₂ dimers deposited on defective h-BN were determined. Then, sensitivities of 3d-metal dimers-doped h-BN structures towards the NO and CO gases were investigated. It was found that the interaction energies of these 3d-metal dimers embedded on defective h-BN are higher than those deposited on pristine h-BN structure, which indicates that the 3d-metal dimers exhibit good stability on defective h-BN. Moreover, this work demonstrated that the CO and NO adsorption energies on 3d-metal dimers-doped h-BN structures are higher than those computed in the literature for pristine h-BN structure. Consequently, the here considered 3d-metal dimers-doped h-BN structures can be good candidates for toxic CO and NO gas detection.

Elongation (ELG) method-based property optimization (POPT) is an effective approach for designing large systems from one terminal to the other. An alternating multi-directional ELG method is introduced to enable alternating POPT for complex systems with multiple growth directions, improving efficiency. (Hyper)polarizabilities of donor-acceptor-substituted polydiacetylenes (PDAs) aligned along the $��z�$-axis are optimized by alternating POPT, where donor- and acceptor-substituted diacetylene monomers are alternately selected and attached to two PDA terminals. Alternating POPT's capability in designing systems with expected properties, efficiency, and accuracy has been validated. For $��n�$ types of donor and acceptor groups, the existing simultaneous ELG-based POPT requires calculating $��{�n�}^{�2�}�$ combinations, as monomers are simultaneously attached in both elongation directions. In contrast, the alternating ELG-based POPT only requires $��2�n�$ combinations, halving the number of basis functions involved in calculations and significantly enhancing efficiency.

This study introduces a computational protocol for modeling the emission spectra of exciplexes using excited-state ab initio molecular dynamics (AIMD) simulations. The protocol is applied to a model exciplex formed by oligo-p-phenylenes (OPPs) and triethylamine (TEA), which is of interest in the context of photocatalytic reduction of $��{�\text{CO}�}_{�2�}�$. AIMD facilitates efficient sampling of the conformational space of OPP3 and OPP4 exciplexes with TEA, offering a dynamic alternative to previously employed static methods. The AIMD-based protocol successfully reproduces experimental emission spectra for OPP-TEA exciplexes, agreeing with previous computational and experimental findings. The results show that AIMD simulations provide an efficient means of sampling the conformational space of these exciplexes, requiring less user input and, in some instances, fewer computational resources than multiple excited-state optimizations initiated from user-specified initial structures. The study also evaluates the yield of intersystem crossing (ISC) using AIMD and Landau-Zener probability. The results suggest that ISC is a minor decay channel for OPP3 and OPP4. This work provides new insights into the structural flexibility and emission characteristics of OPP-TEA photoredox catalyst systems, potentially contributing to improved design strategies for organic chromophores in $��{�\text{CO}�}_{�2�}�$ reduction applications.

Diphenylamine (DPA) derivatives, used as antioxidants in rubber-based products, inhibit autoxidation by donating hydrogen atoms to peroxyl radicals. Octanol–water partition coefficient (LogK_ow), an antioxidant index, helps predict their distribution in hydrophobic polymer matrices. Therefore, this study aimed to investigate the relationship between the structure of DPA derivatives and their antioxidant activities, using machine learning with quantitative structure–activity relationships (QSAR) and quantum mechanics (QM). The structure of DPA derivatives was optimized using Density Functional Theory and analyzed for molecular properties. The QSAR models were trained using important descriptors identified through permutation importance. Among the models developed, the Gradient Boosting Regressor (GBR) showed the best performance, with R² of 0.983 and root mean square error (RMSE) of 0.642 for the test set. SHAP analysis revealed that molecular weight and electronic properties significantly influenced LogK_ow predictions. For instance, a higher molecular weight was associated with increased LogK_ow, and a higher positive charge of C2 correlated with higher LogK_ow predictions. Consequently, the two potent compounds (D1 and D2) were designed based on QSAR model guidance. The GBR model predicted LogK_ow values of 9.789 and 7.102 for D1 and D2, respectively, which are higher than the training compounds in the model. To gain molecular insight, the quantum chemical calculations with M062X/6–311++G(d,p)//M062X/6-31G(d,p) were performed to investigate the bond dissociation enthalpy (BDE). The results showed that D1 (79.50 kcal/mol) and D2 (72.43 kcal/mol) exhibited lower BDEs than the reference compounds, suggesting that the designed compounds have the potential for enhanced antioxidant activity. In addition, the antioxidant reaction mechanism was studied by using M062X/6–311++G(d,p)//M062X/6-31G(d,p) which found that the hydrogen atom transfer is the key step, where D1 and D2 showed activation energy barriers of 10.38 and 6.29 kcal/mol, respectively, compared to reference compounds of R3 (10.39 kcal/mol), R1 (10.40 kcal/mol), and R2 (18.26 kcal/mol). Therefore, our findings demonstrate that integrating QSAR with quantum chemical calculations can effectively guide the design of DPA derivatives with improved antioxidant properties.