Journal of Computational Chemistry最新文献

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.