Journal of Chemical Theory and Computation最新文献

Computer simulations play a pivotal role in interpreting experimental two-photon absorption (2PA) spectra. One of the key aspects of the simulation of these spectra is to take into account the vibrational fine structure of the bands in electronic spectra. This is typically done by employing Franck-Condon (FC) term and low-order terms in the Herzberg-Teller (HT) expansion. In this work, we present a systematic study of first-order HT effects on the vibronic structure of π → π* electronic bands in 2PA spectra of 13 common fluorophores. We begin by evaluating the performance of several density functional approximations (DFAs) against the second-order coupled cluster singles and doubles model (CC2) for reproducing two-photon transition moments and their first- and second-order derivatives with respect to normal modes of vibration on a set of six donor-acceptor molecules. Our findings reveal that most DFAs produce inaccurate values for these derivatives, with the exception of the LC-BLYP functionals with range-separation parameters of 0.33 and 0.47. Although these functionals underestimate the HT contribution to the 2PA total intensities of the π → π* electronic bands, they offer a reasonable qualitative reproduction of the HT vibrational fine structure of the reference spectra. We further explore HT effects on fluorescent chromophores, finding that HT contributions are secondary to FC effects, leading to small shifts of the wavelengths peaks, and minimal changes in the intensities. Additionally, the adiabatic Hessian, vertical Hessian, and vertical gradient vibronic models are assessed. The general agreement among these models confirms that the harmonic approximation is suitable for studying the selected fluorophores.

Quantum computing (QC) provides a promising avenue for enabling quantum chemistry calculations, which are classically impossible due to computational complexity that increases exponentially with system size. As fully fault-tolerant algorithms and hardware, for which an exponential speedup is predicted, are currently out of reach, recent research efforts have been dedicated to developing and scaling algorithms for Noisy Intermediate-Scale Quantum (NISQ) devices to showcase the practical usefulness of such machines. To demonstrate the usefulness of NISQ devices in the field of chemistry, we apply our recently developed FAST-VQE algorithm and a state-of-the-art quantum gate reduction strategy based on propositional satisfiability together with standard optimization tools for the simulation of the rate-determining proton transfer step for CO₂ hydration catalyzed by carbonic anhydrase resulting in the first application of a quantum computing device for the simulation of an enzymatic reaction. To this end, we have combined classical force field simulations with quantum mechanical methods on classical and quantum computers in a hybrid calculation approach. The presented technique significantly enhances the accuracy and capabilities of QC-based molecular modeling and finally pushes it into compelling and realistic applications. The framework is general and can be applied beyond the case of computational enzymology.

We present an algorithmic approach to optimize chain propagator computations in polymer field theory simulations, including self-consistent field theory (SCFT) calculations and field-theoretic simulations (FTSs). Propagator calculations for branched block copolymers often involve recursive structures and overlapping subproblems, resulting in redundant computations. By employing dynamic programming (DP) and encoding computational dependencies as strings, our method systematically eliminates these redundancies in mixtures of branched polymers. The algorithm achieves optimal time complexity for various polymeric systems, including star-shaped, comb, dendrimer polymers, and homopolymer mixtures, by reusing and aggregating propagators for symmetric and repetitive structures. This enhances computational efficiency and reduces memory usage, addressing a key limitation in developing versatile polymer field theory simulation software. Our approach streamlines the simulation of complex branched polymers without requiring manual software adjustments, facilitating more efficient workflows for polymer researchers. Furthermore, the method enables automated searches for inverse design by optimizing computations across diverse branched polymer architectures, contributing to the discovery and design of novel polymeric materials. The algorithm is implemented in open-source software, ensuring accessibility for further development and broader application in computational polymer science.

Spatial derivatives of the natural orbitals (NOs) at their nodal surfaces are shown to encode information about the on-top two-electron density Φ₂(r⃗) in an approximate manner. This encoding, which becomes exact at the limit of an infinite number of nodal surfaces, allows the reconstruction of Φ₂(r⃗) up to a multiplicative constant that can be retrieved from an identity involving the NO in question and its occupation number. This reconstruction provides a new consistency check for electronic structure formalisms, such as the one-electron reduced density matrix theory, that employ NOs as primary quantities.

We present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a Variational Autoencoder (VAE), and two converter networks establish a bidirectional mapping between the polymer parameters (bending modulus, stretching force, and steady shear) and the scattering functions. The training data are generated using off-lattice Monte Carlo simulations to avoid the orientational bias inherent in lattice models, ensuring robust sampling of polymer conformations. The feasibility of this bidirectional mapping is demonstrated by the organized distribution of polymer parameters in the latent space. By integrating the converter networks with the VAE, we obtain a generator that produces scattering functions from given polymer parameters and an inferrer that directly extracts polymer parameters from scattering data. While the generator can be utilized in a traditional least-squares fitting procedure, the inferrer produces comparable results in a single pass and operates 3 orders of magnitude faster. This approach offers a scalable automated tool for polymer scattering analysis and provides a promising foundation for extending the method to other scattering models, experimental validation, and the study of time-dependent scattering data.

Accurate modeling of metal polarization is crucial for understanding molecular interactions at metal–liquid interfaces. In this paper, we present a novel computational method for incorporating the polarization of metallic electrons into classical molecular dynamics simulations. Our approach employs a kernel-based polarization model to describe the real-time polarization of the metal electron density on a three-dimensional grid, with parameters fitted to quantum mechanical calculations. We applied this model to investigate the water–Au(111) interface, analyzing the effects of varying levels of metal polarization: (1) no polarization, (2) full polarization, and (3) time-averaged polarization. The results showed that metal electron polarization enhanced the orientational fluctuations of water molecules, stabilized the O-down configuration near the metal surface, and increased the population of nondonor hydrogen-bond configurations. The time-averaged approximation reproduces some trends observed with full polarization but introduces a bias toward lay-down configurations, leading to an overestimation of double-donor configurations. Our grid-based polarization method offers a computational approach for simulating metal polarization effects, providing new methods to investigate the electrostatics and dynamics of metal–liquid interfaces.

We present an extended Lagrangian shadow molecular dynamics scheme with an interatomic Born-Oppenheimer potential determined by the relaxed atomic charges of a second-order charge equilibration model. To parametrize the charge equilibration model, we use machine learning with neural networks to determine the environment-dependent electronegativities and chemical hardness parameters for each atom, in addition to the charge-independent energy and force terms. The approximate shadow molecular dynamics potential in combination with the extended Lagrangian formulation improves the numerical stability and reduces the number of Coulomb potential calculations required to evaluate accurate conservative forces. We demonstrate efficient and accurate simulations with excellent long-term stability of the molecular dynamics trajectories. The significance of choosing fixed or environment-dependent electronegativities and chemical hardness parameters is evaluated. Finally, we compute the infrared spectrum of molecules via the dipole autocorrelation function and compare to experiments to highlight the accuracy of the shadow molecular dynamics scheme with a machine learned flexible charge potential.

Charge transfer (CT) processes that are electronically nonadiabatic are ubiquitous in chemistry, biology, and materials science, but their theoretical description requires diabatic states or adiabatic excited states. For complex systems, these latter states are more difficult to calculate than the adiabatic ground state. Here, we propose a simple method to obtain diabatic states, including energies and charges, by constraining the atomic charges within the charge equilibration framework. For two-state systems, the exact diabatic coupling can be determined, from which the adiabatic excited-state energy can also be calculated. The method can be viewed as an affordable alternative to constrained density functional theory (CDFT), and so we call it constrained charge equilibration (CQEq). We test the CQEq method on the anthracene-tetracyanoethylene CT complex and the reductive decomposition of ethylene carbonate on a lithium metal surface. We find that CQEq predicts diabatic energies, charges, and adiabatic excitation energies in good agreement with CDFT, and we propose that CQEq is promising for combination with machine learning force fields to study nonadiabatic CT in the condensed phase.

We present the first analytic-derivative-based formulation of vibrational circular dichroism (VCD) atomic axial tensors for second-order Mo̷ller-Plesset (MP2) perturbation theory. We compare our implementation to our recently reported finite-difference approach and find close agreement, thus validating the new formulation. The new approach is dramatically less computationally expensive than the numerical derivative method with an overall computational scaling of $O\left(N^6\right)$. In addition, we report the first fully analytic VCD spectrum for (S)-methyloxirane at the MP2 level of theory.

While kinetic generators produce thermo-kinetic data for detailed gas-phase kinetic models, adapting these models for liquid-phase applications poses challenges due to the need for solvent-dependent thermodynamic properties. To bridge this gap, solvation energies are used to incorporate solvent effects into gas-phase thermo-kinetic data. However, such an adaptation depends on calculating liquid-phase data of unconventional solutes such as free radicals and transition states, which are not accessible with classical equations of states. To address this issue, this work proposes a flexible framework based on an equation of state that integrates all the latest advances of this model family and is called the tc-PR EoS. Combined with a quantum-based continuum solvation model (COSMO-RS) through an advanced mixing rule, the proposed model is made predictive by employing group contribution methods to estimate the pure compound input parameters required to perform thermodynamic calculations with the model. These parameters can be calculated for closed-shell molecules, free radicals, and transition states, with an average deviation of less than 10% with respect to the benchmark database containing experimental data as well as data obtained from quantum-based calculations and QSPR-type correlations. The tc-PR/COSMO-RS model is able to predict the solvation free energies of activation for H-abstraction reactions with an accuracy of approximately 0.2 kcal/mol, offering a high-throughput and accurate solution for integrating solvation effects into detailed kinetic models in the liquid phase.