The Journal of Physical Chemistry A最新文献

Planar tetracoordinate oxygen (ptO) represents a significant challenge in hypercoordinate chemistry due to oxygen’s high electronegativity and strong preference for localized bonding. While recent studies have revealed stabilization mechanisms dominated by electrostatic interactions, the design of ptO clusters with inert, functional peripheral frameworks, particularly those incorporating noble metals, remains unexplored. Herein, we theoretically design a star-like dianion cluster, OLi₄Au₄^2–, which features a central ptO atom electrostatically confined within a Li₄ square, itself armored by a rigid ring of gold atoms. Combined density functional theory and coupled-cluster calculations identify this D_4h-symmetric structure as a global minimum. Bonding analysis confirms the dominance of electrostatic interactions, with negligible covalent character between O and Li. Born–Oppenheimer molecular dynamics simulations attest to its excellent dynamic stability. This work introduces the first ptO cluster stabilized by a noble metal framework, offering a novel design paradigm for planar hypercoordinate systems with customizable peripheries and potential applications.

We present a configuration interaction (CI) framework in which the electronic Hamiltonian is expressed in a basis of charge-localized determinants. This is used to independently generate adiabatic CI states and charge-localized CI states, both of which are unambiguously defined through a diagonalization procedure. The CI framework offers a simple interpretation of adiabatic states as resonance hybrids of different electron distributions, providing a simple picture for discussing charge delocalization in chemical bonding. The charge-localized states serve as a convenient orthogonal representation of initial and final states in electron transfer processes and provide a definition of their electronic coupling. These two models enable an analysis of the water dimer hydrogen bond. While there has been a longstanding debate on the amount of charge transfer in a water dimer, the pertinent question is the importance of ionic contributions to the wave function. We demonstrate that although the overall charge transfer is small (on the millielectron scale), the occurrence of particular ionic contributions is crucial to get the correct potential energy surface.

The H-abstraction reactions by small molecule radicals constitute a critical step in the low-temperature chain propagation during 3-pentanone oxidation. This work presents a kinetic investigation of H-, CH₃-, NH₂-, HO₂-, OH-, and NO₂-mediated H-abstraction from 3-pentanone, employing CCSD(T)/CBS calculations integrated with RRKM and TST. Sixteen reaction channels are explored to elucidate the reaction mechanisms. All reaction pathways exhibit energy barriers within the range of 1.1–37.2 kcal/mol. The H-, CH₃-, NH₂-, and OH-mediated H-abstractions are exothermic, whereas those by HO₂ and NO₂ are endothermic. For H, CH₃, NH₂, HO₂, and NO₂ radicals, the barrier heights increase from the 1-site to the 2-site, while the opposite trend is observed for OH. The prevailing reaction site of 3-pentanone migrates from one site to another with increasing temperature. The OH process displays the lowest barriers, leading to the highest rate constants, while the NO₂-mediated pathways are kinetically unfavorable. Furthermore, the integration of calculated data into the 3-pentanone oxidation model improves predictive accuracy in matching ignition delay times measured in experiments. The sensitivity analysis identifies dominant consumption pathways and key reactions in 3-pentanone oxidation.

Reliable explosion modeling requires accurate thermodynamic properties of hazardous chemicals, yet the scarcity of experimental data highlights the urgent need for predictive approaches to bridge this critical gap. In recent years, machine learning-assisted quantitative structure–property relationship (ML-QSPR) approaches have been widely adopted for thermodynamic property prediction due to their high computational efficiency and strong interpretability. Nevertheless, current methods still suffer from limited multitask prediction accuracy, redundant features, and poor generalization under small-sample conditions and descriptor calculation constraints. In this work, we propose a thermodynamically constrained multitask learning network designed for small-sample thermodynamic property prediction. The model leverages multitask learning to exploit inherent correlations among thermodynamic properties, integrates ensemble learning strategies into the feature engineering process to alleviate overfitting, and incorporates physicochemical constraints into the loss function by constructing a physics-informed neural network (PINN) to enhance structure–property relationship modeling. Comparative experiments demonstrate that ThermoMTLnet outperforms traditional machine learning models and single-task neural networks across multiple metrics, including Pearson correlation coefficient (PCC) and mean absolute error (MAE). Furthermore, ThermoMTLnet achieved a 0.23% higher PCC and 2.44% lower MAE than the best baseline on 300-sample data sets, while maintaining consistent superiority on larger data sets. This demonstrates its robust generalization capability for thermodynamic property prediction with limited samples.

Cu(I)-bisdiimine complexes, [Cu(NN)₂]⁺ (where NN represents a diimine such as phenanthroline or bipyridine derivatives), are considered promising photosensitizers for various photochemical applications. However, their effectiveness is subject to several key challenges. In particular, controlling steric strain around the copper(I) center by introducing appropriate substituents (R) at the α-position of the nitrogen atoms is crucial for optimizing the excited-state properties of the complex. In brief, increasing the size of R leads to longer emission lifetimes and higher quantum yields. Additionally, the energy of the singlet excited state rises with increasing steric bulk, enhancing photoinduced reactivity. However, excessive steric strain from bulky substituents can significantly destabilize the coordination sphere. To balance complex stability with increased steric bulk around the metal center, we have developed two novel nonsymmetrical ligands featuring branched alkyl chains and benzyl groups at the α-position of the nitrogen atoms. Our findings demonstrate that intramolecular π-stacking interactions between the benzyl group and the opposing phenanthroline ligand contribute to stabilizing the coordination sphere. Furthermore, the flexibility of the benzyl group reinforces the tetrahedral geometry around copper(I), resulting in an increased singlet excited-state energy compared to benchmark complexes. Notably, we show that this enhancement in excited-state energy translates into greater excited-state reactivity.

The gas-phase hydrogen abstraction reaction kinetics of atmospheric volatile organic compounds (VOCs) have been investigated using multiconformer transition state theory (MC-TST) as part of the development of the Jammy Key for Transition States (JKTS), an automated tool developed to address the vast number of organic species in the atmosphere that constantly undergo reactions with radicals. The rate constants for OH-initiated reactions with several short-chain compounds─methane, ethane, propane, and their corresponding alcohols and carbonyls─were computationally determined and compared to experimental data. Additionally, the OH abstraction kinetics of pinonaldehyde, a key oxidation product of biogenic VOCs, were studied in detail. Tunnelling effects were evaluated using Wigner and Eckart tunnelling corrections to ensure accurate prediction of reaction rates. JKTS yielded rate constants within a factor of ∼2–3 of experimental data across all systems studied, with branching ratios for pinonaldehyde showing significant contributions from aldehydic and tertiary hydrogen abstraction pathways. The calculated rate constants for pinonaldehyde, 1.739 × 10^–11 cm³ molecule^–1 s^–1 (Eckart) and 1.847 × 10^–11 cm³ molecule^–1 s^–1 (Wigner), align well with the experimental values of (4–9) × 10^–11 cm³ molecule^–1 s^–1 at room temperature. These results demonstrate the capability of JKTS to automate the computation of reaction kinetics and support its application in atmospheric chemistry for accurate modeling of VOC oxidation mechanisms.

The first direct evidence that the hydrolysis reaction of uranium hexafluoride (UF₆) follows multiple competing pathways which are driven by the ratio of water to UF₆, temperature, and isotopic composition is presented. Using temperature dependent infrared spectroscopy, it is shown the hydrolysis can be prevented at temperatures below 150 K, and that water-rich environments promote the formation of uranium oxyfluoride intermediates. Spectral shifts reveal isomeric transitions and the growth of polymeric species, with reaction reversibility observed at high water concentrations. Additionally, controlled heating rates affect the emergence of intermediates. The final particulate product consistently forms as uranyl fluoride hydrate, though its morphology and spectral signature vary with reaction conditions and annealing. These findings help clarify long-standing uncertainties surrounding UF₆ hydrolysis.

The gas-phase structures of capped phenylated polyalanine peptides, Ac-Ala-Ala-Phe-Ala-NH₂ (AAFA) and Ac-Ala-Ala-Phe-Ala-Ala-NH₂ (AAFAA), were investigated using conformer-selective IR–UV ion dip spectroscopy employing the IR light of the FELIX free electron laser. IR absorption spectra were measured in the wide 300–1900 cm^–1 range and additionally in the 3200–3600 cm^–1 region, complemented by extensive quantum-chemical calculations. The AAFA peptide was found to adopt a single dominant conformer with a β-hairpin structure stabilized by four hydrogen bonds, whose predicted spectrum closely matches the experimental data. In contrast, no conformer of AAFAA matches the experimental spectrum, despite generating over 200,000 conformers across multiple search strategies, suggesting that the true structure was not found. Additionally, computations of the molecules with and without the phenyl group reveal an induced alteration of the conformational landscape.

The conformational space of 2-chloropropionic acid was reinvestigated using millimeter-wave and chirp-polarization Fourier transform microwave spectroscopies under isolated conditions in supersonic expansions. Besides the global minimum (carbonyl oxygen eclipsed with the methyl group), two additional conformers were identified: one with the carbonyl eclipsed relative to the α-C–H bond, and another stabilized by an intramolecular OH···Cl hydrogen bond. The latter interaction is evidenced by a decrease in |χ_zz|: the chlorine nuclear quadrupole coupling constant associated with the principal axis lying along the C–Cl bond and an increase in the asymmetry parameter η = (χ_xx – χ_yy)/χ_zz of the chlorine quadrupole coupling tensor. Rotational constants, geometries, and relative energies were determined and compared with quantum-chemical calculations, revealing larger deviations for conformers with intramolecular interactions. Calculations also show that chlorination markedly modifies the conformational landscape relative to propionic acid, affecting both minima and interconversion barriers. The accurate reproduction of the electronic properties encoded in the determined complete nuclear quadrupole coupling tensors requires the inclusion of relativistic approaches.

Understanding how atmospheric molecular clusters form and grow is key to resolving one of the biggest uncertainties in climate modeling: the formation of new aerosol particles. While quantum chemistry offers accurate insights into these early-stage clusters, its steep computational costs limit large-scale exploration. In this work, we present a fast, interpretable, and surprisingly powerful alternative: the k-nearest neighbor (k-NN) regression model. By leveraging chemically informed distance metrics, including a kernel-induced metric and one learned via metric learning for kernel regression (MLKR), we show that simple k-NN models can rival more complex kernel ridge regression (KRR) models in accuracy while reducing computational time by orders of magnitude. We perform this comparison with the well-established Faber–Christensen–Huang–Lilienfeld (FCHL19) molecular descriptor; however, other descriptors (e.g., FCHL18, MBDF, and CM) can be shown to have similar performance. Applied to both simple organic molecules in the QM9 benchmark set and large data sets of atmospheric molecular clusters (sulfuric acid–water and sulfuric–multibase–base systems), our k-NN models achieve near-chemical accuracy, scale seamlessly to data sets with over 250,000 entries, and even appears to extrapolate to larger unseen clusters with minimal error (often nearing 1 kcal/mol). With built-in interpretability and straightforward uncertainty estimation, this work positions k-NN as a potent tool for accelerating discovery in atmospheric chemistry and beyond.