Journal of Chemical Theory and Computation最新文献

Planar hexacoordination is an extremely uncommon phenomenon for the atoms that belong to the main group. Within this article, we have analyzed the potential energy surfaces (PES) of ABeC₂B₄ (A = N, P, As, Sb, and Bi) clusters in neutral, monocationic, monoanionic, dicationic, and dianionic states using density functional theory (DFT). Among which PBeC₂B₄, PBeC₂B₄^-, AsBeC₂B₄^-, AsBeC₂B₄^2-, SbBeC₂B₄^-, and BiBeC₂B₄^- clusters contain a planar hexacoordinate boron (phB) atom in the global minimum energy structures with C_s symmetry. The global minima of the remaining clusters do not correspond to a phB atom. According to the results of the natural charge computations, a significant amount of negative charge is accumulated on the phB atom for each global minimum. Based on the values of the nucleus independent chemical shift (NICS), the phB structures are predicted to possess σ/π-dual aromaticity. The most intriguing aspect is that the planarity of the phB core is preserved in the complexes that are coupled to the N-heterocyclic carbene (NHC) ligand. The stability of these complexes has been depicted here for the first time. As a result, it is our hope that both bare and ligand-stabilized clusters are viable options for gas-phase observation and large-scale synthesis, respectively.

We present a procedure for enhanced sampling of molecular dynamics simulations through informed stochastic resetting. Many phenomena, such as protein folding and crystal nucleation, occur over time scales inaccessible in standard simulations. We recently showed that stochastic resetting can accelerate molecular simulations that exhibit broad transition time distributions. However, standard stochastic resetting does not exploit any information about the reaction progress. For a model system and chignolin in explicit water, we demonstrate that an informed resetting protocol leads to greater accelerations than standard stochastic resetting in molecular dynamics and Metadynamics simulations. This is achieved by resetting only when a certain condition is met, e.g., when the distance from the target along the reaction coordinate is larger than some threshold. We use these accelerated simulations to infer important kinetic observables such as the unbiased mean first-passage time and direct transit time. For the latter, Metadynamics with informed resetting leads to speedups of 2-3 orders of magnitude over unbiased simulations with relative errors of only ∼35-70%. Our work significantly extends the applicability of stochastic resetting for enhanced sampling of molecular simulations.

The prediction of a specific chemical property across a vast library of derivatives represents a formidable challenge. Conventional computational methodologies typically rely on brute-force calculations involving the computation of the property of interest for the entire library or a significant subset. In this study, we present a novel phenomenological approach to address this challenge, employing a perturbation theory-like framework to describe substituent effects. This proposed methodology has the potential to forecast the molecular properties of millions of compounds based on information derived from just a few hundred. This method is applied to the design of molecular solar thermal (MOST) systems, which are devices permitting harvesting solar energy and storing it in a chemical form. The optimization of MOST performance is a critical issue in practical applications of this technology, so exploration of large libraries of derivatives at low computational cost is an interesting approach to tackle the problem. To accomplish this objective, we explore the functionalization of the norbornadiene/quadricyclane (NBD/QC) system utilizing the proposed perturbational approach predicting the energy of 350 derivatives from small sets of 5 and 50 calculated compounds.

A generalized extraction procedure for magnetic interactions using effective Hamiltonians is presented that is applicable to systems with more than two sites featuring local spins S_i ≥ 1. To this end, closed, nonrecursive expressions pertaining to chains of arbitrary equal spins are derived with the graphical method of angular momentum. The method is illustrated by extracting magnetic couplings from ab initio calculations on a [CaMn₃^(IV)O₄] cubane. An extension to nonsequential coupling schemes proves conducive to expressing additional symmetries of certain spin Hamiltonians.

Biogas, primarily composed of methane (CH₄) and carbon dioxide (CO₂), is considered an alternative renewable energy resource. Efficient CO₂/CH₄ separation is essential for biogas upgrading to increase energy density, and in this context, metal-organic frameworks (MOFs) have demonstrated significant potential. Here, we integrate multiscale modeling with cross-diversity machine learning (ML) to unveil MOFs with open copper sites (OCS-MOFs) that exhibit exceptional CO₂/CH₄ separation performance. Our focus on diversity-adaptable ML guarantees that ML models trained in one chemical space are rigorously transferable to unseen MOFs from distinct chemical spaces, assuring their robustness in real-world applications. By leveraging a meticulously curated data set of 27592 OCS-MOFs, we develop ML models with high predictive accuracy, capable of identifying top-performing OCS-MOFs across diverse chemical environments. This work not only elucidates the reticular chemistry that governs optimal CO₂/CH₄ separation performance in OCS-MOFs but also establishes a new benchmark for scalable and resilient digital MOF discovery, with cross-diversity accuracy as the key determinant of model transferability.

Molecular dynamics (MD) simulations are essential for understanding molecular phenomena at the atomic level, with their accuracy largely dependent on both the employed force field and sampling. Polarizable force fields, which incorporate atomic polarization effects, represent a significant advancement in simulation technology. The polarizable Gaussian multipole (pGM) model has been noted for its accurate reproduction of ab initio electrostatic interactions. In this study, we document our effort to enhance the computational efficiency and scalability of the pGM simulations within the AMBER framework using MPI (message passing interface). Performance evaluations reveal that our MPI-based pGM model significantly reduces runtime and scales effectively while maintaining computational accuracy. Additionally, we investigated the stability and reliability of the MPI implementation under the NVE simulation ensemble. Optimal Ewald and induction parameters for the pGM model are also explored, and its statistical properties are assessed under various simulation ensembles. Our findings demonstrate that the MPI-implementation maintains enhanced stability and robustness during extended simulation times. We further evaluated the model performance under both NVT (constant number, volume, and temperature) and NPT (constant number, pressure, and temperature) ensembles and assessed the effects of varying timesteps and convergence tolerance on induced dipole calculations. The lessons learned from these exercises are expected to help the users to make informed decisions on simulation setup. The improved performance under these ensembles enables the study of larger molecular systems, thereby expanding the applicability of the pGM model in detailed MD simulations.

Recent advances in machine learning have facilitated numerically accurate solution of the electronic Schrödinger equation (SE) by integrating various neural network (NN)-based wave function ansatzes with variational Monte Carlo methods. Nevertheless, such NN-based methods are all based on the Born-Oppenheimer approximation (BOA) and require computationally expensive training for each nuclear configuration. In this work, we propose a novel NN architecture, SchrödingerNet, to solve the full electronic-nuclear SE by defining a loss function designed to equalize local energies across the system. This approach is based on a translationally, rotationally and permutationally symmetry-adapted total wave function ansatz that includes both nuclear and electronic coordinates. This strategy not only allows for an efficient and accurate generation of a continuous potential energy surface at any geometry within the well-sampled nuclear configuration space, but also incorporates non-BOA corrections, through a single training process. Comparison with benchmarks of atomic and small molecular systems demonstrates its accuracy and efficiency.

Hydrogen-atom transfer is crucial in a myriad of chemical and biological processes, yet the accurate and efficient description of hydrogen-atom transfer reactions and kinetic isotope effects remains challenging due to significant quantum effects on hydrogenic motion, especially tunneling and zero-point energy. In this paper, we combine transition state theory (TST) with the recently developed constrained nuclear-electronic orbital (CNEO) theory to propose a new transition state theory denoted CNEO-TST. We use CNEO-TST with CNEO density functional theory (CNEO-DFT) to predict reaction rate constants for two prototypical gas-phase hydrogen-atom transfer reactions and their deuterated isotopologic reactions. CNEO-TST is similar to conventional TST except that it employs constrained minimized energy surfaces to include zero-point energy and shallow tunneling effects in the effective potential. We find that the new theory predicts reaction rates quite accurately at room temperature. The effective potential surface must be generated by CNEO theory rather than by ordinary electronic structure theory, but because of the favorable computational scaling of CNEO-DFT, the cost is economical even for large systems. Our results show that dynamics calculations with this approach achieve accuracy comparable to variational TST with a semiclassical multidimensional tunneling transmission coefficient at and above room temperature. Therefore, CNEO-TST can be a useful tool for rate prediction, even for reactions involving highly quantal motion, such as many chemical and biochemical reactions involving transfers of hydrogen atoms, protons, or hydride ions.

The coupling of matter to the quantized electromagnetic field of a plasmonic or optical cavity can be harnessed to modify and control chemical and physical properties of molecules. In optical cavities, a term known as the dipole self-energy (DSE) appears in the Hamiltonian to ensure gauge invariance. The aim of this work is twofold. First, we introduce a method, which has its own merits and complements existing methods, to compute the DSE. Second, we study the impact of the DSE on cavity-induced nonadiabatic dynamics in a realistic system. For that purpose, various matrix elements of the DSE are computed as functions of the nuclear coordinates and the dynamics of the system after laser excitation is investigated. The cavity is known to induce conical intersections between polaritons, which gives rise to substantial nonadiabatic effects. The DSE is shown to slightly affect these light-induced conical intersections and, in particular, break their symmetry.

Exact exchange contributions included in density functional theory calculations have rendered excellent electronic structure results on both cold and extremely hot matter. In this work, we develop a mixed deterministic-stochastic resolution-of-the-identity compressed exchange (mRICE) method for efficient calculation of exact and hybrid electron exchange, suitable for applications alongside mixed stochastic-deterministic density functional theory. mRICE offers accurate calculations of the electronic structure at a largely reduced computation time compared to other compression algorithms, such as Lin's adaptive compressed exchange, for the warm dense matter. mRICE grants flexibility in the number of exchange compression vectors used to resolve the approximated exchange operator kernel, reducing the computation time of the application of the exchange operator to the vectors by up to 40% while maintaining accuracy in electronic structure predictions. We demonstrate mRICE by computing the density of states of warm dense carbon and neon between temperatures of 10 and 50 eV (116,045 and 580,226 K) and comparing timing and accuracy at varying levels of compression. Finally, we carry out mRICE on the difference between the Fock exchange operator and the semilocal exchange potential kernels and show an enhanced convergence of electronic structure calculations at reduced stochastic sampling.