Journal of Computational Chemistry最新文献

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.

Triarylboranes are among the most important classes of Lewis acid catalysts. However, the molecular design of triarylborane still relies on conventional trial-and-error strategies. This study introduces the virtual borane (VB) method, an original computational approach for the design of triarylborane based on the previously reported virtual ligand strategy. Using quantum chemical calculations, the VB reproduces the electronic and steric properties of various triarylboranes without explicitly requiring knowledge of their molecular structures. This, in turn, enables the rapid parameter-based optimization of the catalyst properties, affording rational and quantitative guidelines for molecular design. The accuracy of the VB method was validated by reproducing the energy diagrams of borane-catalyzed reactions. The utility of this method was demonstrated by virtual borane-assisted optimization, in which the optimal properties for transfer hydrosilylation were rapidly determined by numerical optimization of the VB parameters. The VBAO calculations were used to predict a superior catalyst, which was computationally validated.

This research utilizes first-principles calculations based on density functional theory (DFT) to conduct an in-depth investigation into the atomic structure and reaction mechanisms at the VC(111)/Diamond(111) interface by constructing a model of the VC(111)/Diamond(111) interface. By conducting thorough analyses of interfacial atomic configurations, adhesion energy, interfacial energy, differential charge density, density of states (DOS), and Mulliken population, it is shown that the C-terminated VC(111)/Diamond(111) interface displays stronger interfacial interactions, greater stability, and superior electronic properties compared to the V-terminated interface. Furthermore, the formation of chemical bonds at the interface facilitates a transition from a mechanical interface to a chemically bonded one, thereby contributing to the improvement of thermal conductivity across the composite interface.