Journal of Computational Chemistry最新文献

Quantum chemical methods have been intensively applied to study the pyrolytic conversion of glucose into hydroxymethylfurfural (HMF) and furfural (FF). Herein, we collect the most relevant mechanistic proposals from the recent literature and organize them into a single reaction network. All the transition structures (TSs) and intermediates are characterized using highly accurate ab initio methods and the possible reaction pathways are assessed in terms of the Gibbs energies of the TSs and intermediates with respect to β-glucopyranose, selecting a 2D ideal-gas standard state at 773 K to represent the pyrolysis conditions. Several pathways can lead to the formation of both HMF and FF passing through rate-determining TSs that have ΔG^‡ values of ~49–50 kcal/mol. Both water-assisted mechanisms and nonspecific environmental effects have a minor impact on the Gibbs energy profiles. We find that the HMF → FF + CH₂O fragmentation has a small Δ_rxnG value and an accessible ΔG^‡ barrier. Our computational results, which are in consonance with the kinetic parameters derived from lumped models, the results of isotopic labeling experiments and the reported HMF/FF molecular ratios, could be useful for modeling studies including on nonequilibrium kinetic effects that may render more information about product yields and the relevance of the various pathways.

Sulfonyl fluorides hold significant importance as highly valued intermediates in chemical biology due to their optimal balance of biocompatibility with both aqueous stability and protein reactivity. The Cornella group introduced a one-pot strategy for synthesizing aryl sulfonyl fluorides via Bi(III) redox-neutral catalysis, which facilitates the transmetallation and direct insertion of SO₂ into the BiC(sp²) bond giving the aryl sulfonyl fluorides. We report herein a comprehensive computational investigation of the redox-neutral Bi(III) catalytic mechanism, disclose the critical role of the Bi(III) catalyst and base (i.e., K₃PO₄), and uncover the origin of SO₂ insertion into the Bi(III)C(sp²) bond. The entire catalysis can be characterized via three stages: (i) transmetallation generating the Bi(III)-phenyl intermediate IM3 facilitated by K₃PO₄. (ii) SO₂ insertion into IM3 leading to the formation of Bi(III)-OSOAr intermediate IM5. (iii) IM5 undergoes S(IV)-oxidation yielding the aryl sulfonyl fluoride product 4 and liberating the Bi(III) catalyst for the next catalytic cycle. Each stage is kinetically and thermodynamically feasible. Moreover, we explored other some small molecules (NO₂, CO₂, H₂O, N₂O, etc.) insertion reactions mediated by the Bi(III)-complex, and found that NO₂ insertions could be easily achieved due to the low insertion barriers (i.e., 17.5 kcal/mol). Based on the detailed mechanistic study, we further rationally designed additional Bi(III) and Sb(III) catalysts, and found that some of which exhibit promising potential for experimental realization due to their low barriers (<16.4 kcal/mol). In this regard, our study contributes significantly to enhancing current Bi(III)-catalytic systems and paving the way for novel Bi(III)-catalyzed aryl sulfonyl fluoride formation reactions.

Orbital-optimized coupled-cluster methods are very helpful for theoretical predictions of the molecular properties of challenging chemical systems, such as excited states. In this research, an efficient implementation of the equation-of-motion orbital-optimized coupled-cluster doubles method with the density-fitting (DF) approach, denoted by DF-EOM-OCCD, is presented. The computational cost of the DF-EOM-OCCD method for excitation energies is compared with that of the conventional EOM-OCCD method. Our results demonstrate that DF-EOM-OCCD excitation energies are dramatically accelerated compared to EOM-OCCD. There are almost 17-fold reductions for the $��{�\text{C}�}_{�5�}�{�\text{H}�}_{�12�}�$ molecule in an aug-cc-pVTZ basis set with the RHF reference. This dramatic performance improvement comes from the reduced cost of integral transformation with the DF approach and the efficient evaluation of the particle-particle ladder (PPL) term, which is the most expensive term to evaluate. Further, our results show that the DF-EOM-OCCD approach is very helpful for the computation of excitation energies in open-shell molecular systems. Overall, we conclude that our new DF-EOM-OCCD implementation is very promising for the study of excited states in large-sized challenging chemical systems.

Sampling reference data is crucial in machine learning potential (MLP) construction. Inadequate coverage of local configurations in reference data may lead to unphysical behaviors in MLP-based molecular dynamics (MLP-MD) simulations. To address this problem, this study proposes a new on-the-fly reference data sampling method called radial distribution function (RDF)-based data sampling for MLP construction. This method detects and extracts anomalous structures from the trajectories of MLP-MD simulations by focusing on the shapes of RDFs. The detected structures are added to the reference data to improve the accuracy of the MLP. This method allows us to realize a reasonable MLP construction for liquid water with minimal additional data. We prepare data from an H₂O molecular cluster system and verify whether the constructed MLPs are practical for bulk water systems. MLP-MD simulations without RDF-based data sampling show unphysical behaviors, such as atomic collisions. In contrast, after applying this method, we obtain MLP-MD trajectories with features, such as RDF shapes and angle distributions, that are comparable to those of ab initio MD simulations. Our simulation results demonstrate that the RDF-based data sampling approach is useful for constructing MLPs that are robust to extrapolations from molecular cluster systems to bulk systems without any specialized know-how.

Fungal azaphilones are a broad class of naturally-occurring pigments with diverse applications. Among the azaphilone pigments, mitorubrins are well recognized for their antiviral, antibacterial, antifungal, antiprotozoal, antidiabetic, and antiaging activities in addition to their well-known yellow-orange color. This makes these pigments interesting candidates for use in foods, as cosmetics, and as medicines. In particular, if it is desired to modify the properties of mitorubrin-based pigments, for example by derivatization, it is essential to have an understanding of the electronic spectra of the parent molecules. We have therefore undertaken a computational study of a series of mitorubrins, comparing our computed results with experimental UV/visible spectra. Both density-functional theory (DFT) and coupled-cluster (CC2) methods have been used, and in general, the results are in very good agreement with observation. In order to provide a simple and useful picture of the spectra we analyze the stronger transitions in terms of natural transition orbitals (NTOs).

Predicting protein-ligand binding affinity is a crucial and challenging task in structure-based drug discovery. With the accumulation of complex structures and binding affinity data, various machine-learning scoring functions, particularly those based on deep learning, have been developed for this task, exhibiting superiority over their traditional counterparts. A fusion model sequentially connecting a graph neural network (GNN) and a convolutional neural network (CNN) to predict protein-ligand binding affinity is proposed in this work. In this model, the intermediate outputs of the GNN layers, as supplementary descriptors of atomic chemical environments at different levels, are concatenated with the input features of CNN. The model demonstrates a noticeable improvement in performance on CASF-2016 benchmark compared to its constituent CNN models. The generalization ability of the model is evaluated by setting a series of thresholds for ligand extended-connectivity fingerprint similarity or protein sequence similarity between the training and test sets. Masking experiment reveals that model can capture key interaction regions. Furthermore, the fusion model is applied to a virtual screening task for a novel target, PI5P4Kα. The fusion strategy significantly improves the ability of the constituent CNN model to identify active compounds. This work offers a novel approach to enhancing the accuracy of deep learning models in predicting binding affinity through fusion strategies.

The statistical quantum model (SQM), which assumes that the reactivity is controlled by entrance/exit channel quantum capture probabilities, is well suited for chemical reactions with a long-lived intermediate complex. In this work, a time-independent coupled-channel implementation of the SQM approach is developed for atom-triatom systems in full dimensionality. As SQM treats the capture dynamics quantum mechanically, it is capable of handling quantum effects such as tunneling. A detailed study of the H/D + O₃ capture dynamics was performed by applying the newly developed SQM method on an accurate global potential energy surface. Agreement with previous ring polymer molecular dynamics (RPMD) results on the same potential energy surface is excellent except for very low temperatures. The SQM results are also in reasonably good agreement with available experimental rate coefficients. The strong H/D kinetic isotope effect underscores the dominant role of quantum tunneling under an entrance channel barrier at low temperatures.

We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.

Closo-boron hydrides B_nH_n²⁻ (n = 5–12) are a conceptually well understood class of compounds. For these and a few related prototype compounds, both the local and the global picture of 3-center bonding are extracted from position-space quantities based on the electron density and the pair density. For this purpose, three-center delocalization indices between quantum theory of atoms in molecules (QTAIM) atoms in position space are used to develop a consistent set of local bond and triangle, and global cluster delocalization ratios (DRs), which are quantitatively compared with conceptual Γ values derived from the styx code for each cluster. Combination of the cluster DRs with associated effective numbers of skeletal electron sharing (SES) for selected cluster surface edges, triangles, or the whole cluster yields effective styx type values describing the trend and even the size of the conceptual styx codes for closo-boranes B_nH_n²⁻ and related systems with increasing cluster size n reasonably well. For nonuniform cluster topologies, the different vertex degrees are shown to cause systematic 3-center wise bond delocalization effects for the associated edges and triangles of different average vertex degrees. Extension of DR analysis beyond the styx type triangular cluster-surface bonding corresponds to a triangulation of multicentric bonding. The cluster-wise results keep indicating consistency with the mixed 2- and 3-center bonding approach. The successfully established chemical meaning of the local edge, triangle, and global cluster DRs and their associated SES values constitutes the basis for systematic investigations of mixed 2- and 3-center bonding scenarios in particular in intermetallic and related (endohedral) cluster compounds in the future.

Intramolecular charge transfer (ICT) effects of para-nitroaniline (pNA) in eight solvents (cyclohexane, toluene, acetic acid, dichloroethane, acetone, acetonitrile, dimethylsulfoxide, and water) are investigated extensively. The second-order algebraic diagrammatic construction, ADC(2), ab initio wave function is employed with the COSMO implicit and discrete multiscale solvation methods. We found a decreasing amine group torsion angle with increased solvent polarity and a linear correlation between the polarity and ADC(2) transition energies. The first absorption band involves π → π* transitions with ICT from the amine and the benzene ring to the nitro group, increased by 4%–11% for different solvation models of water compared to the vacuum. A second band of pNA is characterized for the first time. This band is primarily a local excitation on the nitro group, including some ICT from the amine group to the benzene ring that decreases with the solvent polarity. For cyclohexane, the COSMO implicit solvent model shows the best agreement with the experiment, while the explicit model has the best agreement for water.