The Journal of Physical Chemistry A最新文献

Photolysis of energetic materials offers safer and more controllable advantages compared to traditional ignition methods. Tracking the group and electron dynamics during the photolysis of energetic materials is currently a hot and challenging topic. In this work, we used a time-dependent density functional theory (TDDFT) to study the high-order Harmonic generation (HHG) dynamics induced by strong laser interaction with an isolated CH₃NO₂ molecule with varying C-N bond lengths. We found that the elongation of the C-N bond leaves a footprint on the corresponding HHG spectrum. One observed phenomenon is that the overall HHG cutoff position increases with the C-N bond length, and another is a sudden decrease in HHG efficiency at a certain bond length. Our analysis shows that this efficiency drop is due to changes in the electron recombination quantum paths caused by the C-N bond length alteration. Our research provides a new approach to tracking the photolysis process of energetic materials.

Polycyclic aromatic hydrocarbons (PAHs) exhibit intriguing characteristics that position them as promising candidates for advancements in organic semiconductor technologies. Notably, tetracene finds substantial utility in Electronics due to its application in organic light-emitting diodes (OLEDs) and organic field-effect transistors (OFETs). The strategic introduction of an isoelectronic boron-nitrogen (B,N) pair to replace a carbon-carbon pair in acenes introduces changes in the electronic structure, allowing for the controlled modulation of diradical characteristics. Consequently, this B,N substitution enables precise adjustments in chemical, optical, and electronic attributes. In this work, we undertook a systematic exploration of thermally activated delayed fluorescence (TADF) phenomena within a set of 77 B,N-substituted derivatives of tetracene. The primary objective was to identify and select prospective molecules for the fabrication of OLEDs. Employing multiconfigurational methods of computational quantum chemistry, we conducted an extensive investigation to unravel the potential candidates. As a result, we identified molecules that might exhibit the sought-after TADF behavior. Descriptors such as excitation energies, harmonic oscillator model of aromaticity (HOMA) and fractional occupation number weighted density (FOD) were assessed and indicated five candidates with stability comparable to that of pristine tetracene. This research not only contributes to a deeper understanding of the influence of B,N substitution on acene derivatives but also opens doors for the development of organic electronics by harnessing the properties of these selected molecules.

We revisit the naked transition metal cation (Ti⁺) and methanol reaction and go beyond the standard Landau-Zener (LZ) picture when modeling the intersystem crossing (ISC) rate between the lowest doublet and quartet states. We use both (i) unconstrained Born-Oppenheimer molecular dynamics (BOMD) calculations with an approximate two-state method to estimate population transfer between spin diabats and (ii) constrained dynamics to explore energetically accessible portions of the N_DOF - 1 crossing seam, where N_DOF is the total number of internal degrees of freedom. Whereas previous LZ calculations (that necessarily relied on the Condon approximation to be valid) fell short and predicted much slower crossing probabilities than shown in experiment, we show that ISC can occur rapidly because the spin-orbit coupling (SOC) between the doublet and quartet surfaces can vary by 2 orders of magnitude (depending on where in the seam the crossing occurs during dynamics) and the crossing region is revisited multiple times during a dynamics run of a few hundred femtoseconds. We further isolate the two important nuclear coordinates that tune the SOC and modulate the transition, highlighting exactly how and why organometallic ISC can occur rapidly for small systems with floppy internal nuclear vibrational modes.

We have calculated the magnetically induced current density (MICD) susceptibility at the all-electron density functional theory level for a series of coronoid molecules of increasing size and compared the MICD susceptibilities with those calculated using the pseudo-π (PP) model. The molecules sustain global diatropic magnetically induced ring currents (MIRCs), whereas paratropic MICD vortices mainly appear inside the benzene rings. The computationally cheaper PP calculations were also employed on circum[n]coronene molecules showing that the MICD pattern continues to alternate for odd and even n when increasing the size of the molecule. For even n, there is a local paratropic MIRC in the middle of the molecule, whereas when n is odd, the PP models do not sustain any paratropic MIRC pathways. The global diatropic MIRC flowing mainly along the outer edge of the molecule increases with increasing n suggesting that there is no size limit of the MIRC of circum[n]coronene molecules. There are seven weakly aromatic Clar rings in the middle of the PP model of the circum[n]coronene molecules with odd n, whereas circum[n]coronene molecules with even n have no Clar rings. There are no Clar rings in the outer part of the circum[n]coronene molecules with n > 1.

Microkinetic modeling of heterogeneous catalysis serves as an efficient tool bridging atom-scale first-principles calculations and macroscale industrial reactor simulations. Fundamental understanding of the microkinetic mechanism relies on a combination of experimental and theoretical studies. This Perspective presents an overview of the latest progress of experimental and microkinetic modeling approaches applied to gas-solid catalytic kinetics. Then, opportunities and challenges are presented based on recent research progress in gas-solid catalysis and combustion chemistry. For experimental approaches, the importance of ideal catalytic reactors, structured catalysts, and precise elementary rate measurements is emphasized. Additionally, integrating spatiotemporally resolved operando gas-phase diagnostics with surface-adsorbed species characterization methods offers new opportunities for gaining deeper insights into gas-surface reactions. In microkinetic modeling, a hybrid rate parameter evaluation approach that combines first-principles calculations with semiempirical methods, followed by automated mechanism generation and data-driven optimization, opens new avenues for efficiently constructing surface mechanisms. Furthermore, extending microkinetic modeling beyond mean-field approximations allows simulations under realistic catalyst operating conditions. Finally, the critical role of gas-phase mechanisms and comprehensive microkinetic modeling analyses in advancing our fundamental understanding of gas-solid catalytic processes is highlighted.

With the rapid development of thermally activated delayed fluorescence (TADF) materials, achieving efficient reverse intersystem crossing (RISC) to mitigate triplet-triplet annihilation has emerged as a prominent research focus. This study investigates five derivative molecules, featuring varied bridging atoms/groups (O, S, Se, -CH₂-), designed from the reported TADF molecule AC-BO with through-space charge transfer (TSCT) properties. Utilizing time-dependent density functional theory coupled with a PCM solution model, their excited state behaviors were simulated in a toluene environment. Interestingly, it was observed that RISC in AC-BO and one derivative, AC-BCO, occurs predominantly via the T₂ state rather than the typical T₁ state (³LE_B, where B denotes the fluorene bridge), distinguishing it from conventional TSCT-TADF compounds, where RISC typically involves transitions between the ³CT and ¹CT states. This distinctive mode is attributed to reduced spin-orbit coupling (SOC) between ¹CT and ³LE_B, with T₂ representing a significant contributor to the RISC process through its ³CT character. Introduction of heavy atoms enhances the electron-withdrawing ability of the acceptor unit, leading to the T₁ transitions exhibiting ³MRCT characteristics and increased SOC, thereby favoring RISC via ³MRCT to ¹CT transitions. This study not only deepens our understanding of transition mechanisms in TSCT-TADF compounds but also provides crucial insights into the molecular design and regulation of excited triplet states.

The full-dimensional potential energy surface (PES) for the photodissociation of HNCS in the S₁(¹A″) electronic state has been built up by the neural network method based on more than 48,000 ab initio points, which were calculated at the multireference configuration interaction level with Davidson correction using the augmented correlation consistent polarized valence triple-ζ basis set. It was found that two minima, namely, trans and cis isomers of HNCS, and seven stationary points exist on the S₁ PES for the three dissociation pathways: HNCS(S₁) → H + NCS/HNC + S(¹D)/HN(¹Δ) + CS(¹Σ⁺). The dissociation energies of two lowest product channels H + NCS and HNC + S(¹D) calculated on the PES are in good agreement with experimental results, validating the high accuracy of the PES. Furthermore, the quasi-classical trajectory calculations were carried out to investigate the photodissociation dynamics of HNCS(S₁) at the total energy ranging from 5.0 to 6.0 eV based on the newly constructed S₁ PES. It was found that two products H + NCS/HNC + S(¹D) are dynamically comparable with the branching ratios of ∼1:1 at high energies, and the product HNC + S(¹D) is favored at low energies, resulting from different topographies of the PES along the two dissociation pathways. Specifically, the translational energy distributions of the products H + NCS and HNC + S(¹D) were found to be exceedingly different. The former behaves like a Gauss-type function with a broad width and a center of the peak at relatively high energy, while the latter is dominated by the low energies and decays heavily as the translational energy increases, shedding light on the photodissociation dynamics of HNCS in the S₁ band.

Coarse-grained molecular dynamics simulation is widely accepted for assessment of a large complex biological system, but it may also lead to a misleading conclusion. The challenge is to simulate protein structural dynamics (such as folding-unfolding behavior) due to the lack of a necessary backbone flexibility. This study developed a standard coarse-grained model directly from the protein atomic structure and amino acid coarse-grained FF (such as MARTINI FF v2.2). The atomic structure is used as a parent template to set up the coarse model, which naturally gives a better representation of the initial conditions. We have formulated a computational algorithm to set up protein coarse-grained coordinates and force field topology (such as bonds, angles, and dihedrals). The model was validated by a systematic all atom and coarse-grained simulation of a system containing protein human serum albumin and the drug paclitaxel in a water bath. The bonded force constants were optimized locally by neighboring residue-free energy data and globally by history matching against all atom simulation. The coarse-grained model was then applied for several other proteins and justified its general reliability for modeling protein conformations dynamics. We arrived at such a conclusion with great satisfaction because it describes the initial conditions accurately, applies only standard bonded force constants, and provides a significant backbone flexibility.

Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet. This framework integrates group theory, density functional theory-related mechanisms, deep learning techniques, and multiobjective optimization mechanisms, embodying a methodological fusion of data analytics and system optimization. Initially, we focus on atomization energies as the primary target of our study, using the QM9 data set to demonstrate the framework's ability to predict molecular atomization energies with high accuracy and excellent exploration performance. We then further evaluate its predictive capabilities for dipole moments and forces with the QM9X data set, achieving satisfactory results. Additionally, we tested the D3-ImgNet framework on the S_N2 reaction data set to demonstrate its ability to precisely predict the minimum energy paths of S_N2 chemical reactions, showcasing its portability and adaptability in chemical reaction modeling. Finally, visualizations of the electronic density generated by the framework faithfully replicate the physical phenomenon of electron density transfer. We believe that this framework has the potential to accelerate property predictions and high-throughput screening of functional materials.

An adequate understanding of the NO_x interacting chemistry is a prerequisite for a smoother transition to carbon-lean and carbon-free fuels such as ammonia and hydrogen. In this regard, this study presents a comprehensive study on the H atom abstraction by NO₂ from C₃ to C₇ alkynes, dienes, and trienes forming 3 HNO₂ isomers (i.e., TRANS_HONO, HNO₂, and CIS_HONO), encompassing 8 hydrocarbons and 24 reactions. Through a combination of high-level quantum chemistry computation, electronic structures, single-point energies, C-H bond dissociation energies, and 1-D hindered rotor potentials of the reactants, transition state (TS), complexes, and products involved in each reaction are determined at DLPNO-CCSD(T)/cc-pVDZ//M06-2X/6-311++g(d,p), from which potential energy surfaces and energy barriers for each reaction are determined. Following this, the rate coefficients for all studied reactions, over a temperature range from 298 to 2000 K, are computed based on TS theory using the Master Equation System Solver program by considering unsymmetric tunneling corrections. Comprehensive analysis of branching ratios elucidates the diversity and similarities between different species, different HNO₂ isomers, and different abstraction sites. Incorporating the calculated rate parameters into a recent chemistry model reveals the significant influences of this type of reaction on model performance, where the updated model is consistently more reactive for all the alkynes, dienes, and trienes studied in predicting autoignition characteristics. Sensitivity and flux analyses are further conducted, through which the importance of H atom abstractions by NO₂ is highlighted. With the updated rate parameters, the branching ratios in fuel consumption clearly shift toward H atom abstractions by NO₂ while away from H atom abstractions by ȮH. The obtained results emphasize the need for adequately representing these kinetics in new alkyne, diene, and triene chemistry models to be developed by using the rate parameters determined in this study, and call for future efforts to experimentally investigate NO₂ blending effects on alkynes, dienes, and trienes.