Journal of Computational Chemistry最新文献

The electropolymerization of thiophenes can yield high-quality polymer films with tunable properties and a wide array of potential applications. Nevertheless, despite decades of research, the growth mechanism, which involves a complex interplay of interfacial processes including heterogeneous electrochemical reactions, homogeneous chemical reactions, and oligomer solubility, is not well understood. Here, we use experimentally validated density functional theory calculations to survey the geometry, electron distribution, redox, and electronic absorption characteristics of a dataset of thiophene species involved in electropolymerization. The landscape of possible 3-alkylthiophene isomers up to terthiophene is explored, considering three possible redox states with predicted redox potentials. Free energy calculations support radical–radical coupling as the more favorable mechanism, at least in the early stages of polymerization. Time-dependent density functional theory reveals energies and oscillator strengths that can be used for experimental elucidation of the mechanism, for example, via time-resolved spectroscopy, and explains broad absorption features in spectroelectrochemical data. Energetic implications for the formation of branched and regioregular polymers are drawn with new insight into how regioregularity might be achieved via electropolymerization.

The methanolysis of urea represents a promising green route for the synthesis of dimethyl carbonate (DMC), a versatile compound with applications in sustainable chemistry and energy storage. In this work, a comprehensive quantum chemical investigation of the reaction mechanism is presented using density functional theory (DFT), focusing on both uncatalyzed and organotin-catalyzed systems, considering both stepwise and concerted pathways. For MC production, both the stepwise and the concerted mechanisms mediated by a methanol dimer exhibit the lowest activation enthalpies. Consequently, an effective activation enthalpy of 24.0 kcal/mol was determined, in excellent agreement with the experimental value of 23.45 kcal/mol. In contrast, the bimolecular stepwise and concerted models exhibited higher barriers (ΔH^‡ ≈ 42–52 kcal/mol). Entropy values indicated that mechanisms with two methanol molecules involve higher preorganization (ΔS^‡ ≈ −60 cal/mol K), compared to −30 cal/mol K in single-molecule pathways. For DMC production from the methyl carbamate intermediate, the rate-limiting step, it was analyzed with and without an organotin catalyst. Catalysis lowers the activation enthalpy by approximately 10 kcal/mol, yielding a value of 24.9 kcal/mol for the methanol monomer catalyzed system, in good agreement with the experimental ΔH^‡ of 24.3 kcal/mol. To deepen mechanistic understanding, we employed advanced quantum descriptors including reaction force analysis, reaction electronic flux (REF), and natural bond orbital (NBO) charge evolution. These tools revealed synchronous bond rearrangements and electronic polarization effects that govern transition state stability, mainly by the electronic charges of the carbon atom in the carbonyl group and the amine group in the sense C^δ+—N^δ-. This study provides novel mechanistic insights into the dual role of hydrogen bonding and Lewis acid catalysis in DMC synthesis and demonstrates the utility of quantum chemical tools in elucidating complex reaction pathways, offering a foundation for rational catalyst design.

Molecular stacking, especially π-stacking and H-bond interactions, is important in various biochemical and material science fields. Stacking interactions are used to design supramolecular assemblies such as host-guest complexes. Stacking interactions also play a crucial role in the structure and function of biomolecules like DNA, RNA, and proteins. G-quadruplex, a non-canonical DNA structure that is essential for a number of biological functions, such as gene control, telomere preservation, and DNA replication, is stabilized by stacking and H-bond interactions. They are particularly important in cancer development as potential drug targets. Additionally, they are used in drug delivery systems and stimuli-responsive materials. This study aims to cast light on the structure, energetics, and various intermolecular interactions involved in five differently stacked G-quadruplex structures. The various computational methods employed for the calculations and analysis are Energy Decomposition Analysis using DFT calculations and Quantum Theory of Atoms in Molecules (QTAIM) at M062X/6-311G(d,p) level in gas phase. The calculations performed show that the parallel conformations of G-quadruplex are more stable than the other structures containing anti-parallel conformations. Energy decomposition analysis suggests that with respect to stability, the parallel structure benefits more from stacking interactions over the anti-parallel one. However, the H-bond cooperativity contribution in the cyclic G-quadruplex is found to be almost the same in both conformations. The structural stability is further analyzed by the QTAIM method, which shows that the greater number of stacking interactions in the parallel structure makes it more stable than the anti-parallel structure. A detailed examination of the stacking interaction revealed its electrostatic nature.

Global minima of molecular clusters are one of the acute and long-standing problems in the computational chemistry domain. It is important to understand the lowest energy geometry isomer of a given compound for further investigation of physical and chemical properties. The exponential growth of local minima with increasing cluster size makes the problem more relevant. The existing ab initio methods are accurate but very expensive in terms of computational resources. It opens the gap to find more efficient and reliable solutions for rapid exploration. On the other side, neural network potentials are the rising alternatives, with numerically accurate and reliable results for the molecular cluster problem. We integrate AIMNET2, a pretrained model, into our TABU-based PyAR interface. Originally, this model was trained mostly on organic molecules, and we are using it for the PES explorations of molecular clusters, which is a neighboring domain but does not explicitly use any training input. We demonstrate the effectiveness of our methodology by exploring standard molecular clusters of water, ammonia, hydrogen peroxide, methanol, and acetic acid, with aggregation numbers ranging from 1–10. This work represents a significant step toward fully automated computational molecular cluster generation, paving the way for accelerated exploration and discovery in this field.

We present fragmentation-based MP2 and CCSD(T)-level energetic calculations on naturally occurring clathrates encapsulating gas molecules CO$��{}_2�$, CH$��{}_4�$, and H$��{}_2�$S, within a 20-water cage. These calculations are followed by the computation and analysis of their vibrational infrared (IR) spectra. High-accuracy single-point energy evaluations at the MP2 and CCSD(T) levels using the aug-cc-pVNZ basis sets (N = T, Q, 5), involving $��\sim �$6200 basis functions are performed on a desktop workstation with 16 cores, demonstrating the practical feasibility of such demanding computations. To the best of our knowledge, this work reports, for the first time, the complete basis set (CBS) limit at the CCSD(T) level for a system of this size. Our efficient and scalable in-house developed fragment-based algorithms, REAlgo and CIC, enable high-level correlated calculations with large basis sets, opening new avenues for the exploration of intermolecular interactions in complex molecular clusters.

This study systematically evaluates the performance of internally contracted multireference coupled cluster (icMRCC) wave functions constructed using a full-valence complete active space reference as an alternative electronic structure method within the high-accuracy extrapolated ab initio thermochemistry (HEAT) protocol, thereby assessing the accuracy of icMRCC and exploring its potential for highly accurate thermochemical predictions. By substituting single-reference wavefunctions with multireference (MR) alternatives, we aim to capture complex electron correlation effects, particularly in systems with strong static correlations. Using a benchmark dataset of 22 small first-row compounds, we compare the accuracy of different icMRCCSD(T) methodologies with both single-reference their counterparts and experimental data. Our results align with prior findings, confirming that the intrinsic error of the icMRCCSD(T){4}_F method remains well below the chemical accuracy threshold (∼4 kJ mol⁻¹) for thermochemical properties, particularly for atomization energies of molecules with up to 18 correlated electrons. The results underscore the potential of the methods for creating a multireference framework as a high-precision tool for thermochemical applications.