Journal of Computational Chemistry最新文献

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.

Hydrogen bonds are fundamental to molecular structure and function across chemistry, biology, and materials science. Accurate characterization of hydrogen bond geometry, strength, and spectroscopic properties remains a central challenge, particularly for strong hydrogen bonds where quantum delocalization of the bridging proton is significant. In this work, we develop and validate a computational protocol that explicitly accounts for proton delocalization by solving the one-dimensional Schrödinger equation along the proton transfer coordinate for a diverse set of NHN, OHO, and NHO hydrogen-bonded complexes. Quantum mechanical averaging over the proton probability density yields geometric and spectroscopic parameters that are more representative of the true, time-averaged environment probed in experiment. We establish robust correlations between the bridging proton chemical shift and both geometric and energetic descriptors, demonstrating that accounting for nuclear quantum effects leads to more reliable and transferable spectrum–structure relationships than classical approaches. Our results highlight the necessity of including proton delocalization in theoretical descriptions of hydrogen bonds and provide a practical toolkit for the interpretation and prediction of hydrogen bonding effects in complex molecular systems.

Raman Optical Activity (ROA) is an indispensable tool to investigate the chiral characteristics of molecular systems; however, its quantum mechanical modeling remains computationally demanding for large systems. Moreover, standard ab initio packages often become ineffective in such cases. Against this backdrop, we report the application of the fragment-based Molecular Tailoring Approach (MTA) to enable ROA spectra simulations of complex systems (up to 407 atoms) in both gas and solvent phases using density functional theory. The grafting-assisted MTA-based ROA spectra exhibit excellent agreement with the full conventional calculations and successfully reproduce key spectral features. Furthermore, for selected systems, the consistency with the experimental data further validates the effectiveness of the method. Given its accuracy, substantial computational speed-up, and compatibility with off-the-shelf hardware, the present study provides a cost-effective strategy for investigating ROA spectra of large molecular systems.

In the present study, we have explored the capability of an efficient GFN2-xTB meta-dynamics nano-reactor protocol for essential organic transformations in March's Advanced Organic Chemistry. Specifically, we have examined nucleophilic substitution and the related elimination in detail, and we have validated the protocol for electrophilic aromatic substitution, radical substitution, addition to C═C bond, addition to C═O bond, rearrangement, oxidation, and reduction. For an efficient and realistic nano-reaction, we propose the use of a reactive catalyst, a carefully tuned explicit solvation environment, and, if necessary, multiple simulation temperatures. A limitation of the nano-reactor in its current form is the lack of exchange of chemical species between the (simulated) reaction region and the (non-simulated) environment. Another issue is the use of a constant spin multiplicity that prohibits spin crossover that may occur in the real world. One may also need to adjust the size of the spherical wall potential for reactions that necessitate tighter confinement of the reactants. In a case study, we have examined the hydrothermal liquefaction of some lignin models. The nano-reactions account for key products observed in previous experiments, and they enable us to observe mechanisms that may not be trivially conceived. While there is room for development, with our standardized computational settings the nano-reactor protocol already provides an essentially black-box and efficient approach to examine details of a reaction and to provide hints for tuning real-world reaction conditions.

Understanding molecular adsorption and desorption on metal surfaces is crucial for heterogeneous catalysis and surface science. In this work, we systematically evaluate the performance of various density functional theory (DFT) exchange–correlation functionals—PBE, PBE-D3, revPBE-D3, optB88-vdW, BEEF-vdW, and SCAN+rVV10—and a neural network potential for modeling molecular binding relevant to temperature programmed desorption (TPD). Using well-characterized systems, we compare computed bulk lattice constants, surface energies, and binding energies of representative adsorbates (CO, CO₂, methanol, and benzene) on transition metal surfaces (Ni, Cu, Ru, Rh, Pd, Ag, Pt, Au) against experimental TPD data. Our results reveal that dispersion-corrected functionals like PBE-D3 and SCAN+rVV10 yield accurate bulk lattice constants, while BEEF-vdW tends to overestimate them. optB88-vdW and SCAN+rVV10 accurately reproduce surface energies, while PBE-D3 and revPBE-D3 often overestimate them. On the other hand, BEEF-vdW provides better agreement for binding energies but at the cost of less accurate bulk properties. We further demonstrate how machine-learned potentials can efficiently reproduce DFT-level energetics and enable molecular dynamics simulations to extract more realistic kinetic parameters, including pre-exponential factors and potential of mean force (PMF) profiles. This study demonstrates the accuracy of modeling molecular adsorption using DFT and neural network potentials, elucidating the trade-offs associated with functional selection in surface science and offering practical guidance for choosing appropriate computational methods for accurately simulating desorption processes.

Conceptual Density Functional Theory (CDFT) offers a rigorous framework for understanding chemical reactivity through energy-response descriptors. The global electrophilicity index (ω) is particularly useful for rationalizing trends in reactions such as Diels–Alder (DA) cycloadditions. In this work, we present a deep-learning model predicting ω from randomized Coulomb matrices derived from force-field geometries. This enables high-throughput screening with accuracy comparable to DFT but at a fraction of the cost. Validation shows prediction errors below 0.1 eV for nucleophiles and ~0.3 eV for less abundant electrophiles from our PubChem and GDB13 datasets. Diels–Alder activation barriers of a curated set of dienophiles with furan and fulvene correlated well with ω, especially when considering the transition-state interaction energy. Of value for applications in bioconjugation and self-healing polymers, several candidates were identified as more reactive than maleimide (ω = 1.63 eV). Machine-learning models targeting CDFT descriptors thus provide scalable, interpretable tools for automated reaction-space exploration.

In vivo protein-protein association is modulated by non-specific interactions with the cellular matrix, known as crowding. Replicating the heterogeneity and complexity of cellular conditions in experiments and simulations to quantify the crowding influences remains a formidable challenge. As an alternative, homogeneous mixtures of polymers or proteins as crowders are employed, although they provide limited insight to the actual effect. In this work, we present a minimalistic coarse-grained model of GB1 dimerization in lysozyme crowders by adjusting the GB1-lysozyme effective potential parameters obtained through high-resolution Martini simulation. Employing these parameters it is shown that GB1 dimerization (side-by-side dimer formation) is destabilized in presence of lysozyme, as observed in experiments. The model is further extended to incorporate binary crowder mixtures for crowder species of various sizes and interaction potentials. It is found that depending on mixing conditions and crowder volume fraction, dimerization can either be stabilized or destabilized. In particular, in presence of crowders of different sizes, the smaller one plays the dominant role. Finally, we observe that the overall change of stability due to crowding emerges from a delicate balance between enthalpy and entropy that cannot be predicted by considering the property of single crowder species. These results provide invaluable insights for interpreting experimental observations under cell-like conditions.