Journal of Chemical Theory and Computation最新文献

Attaining a complete thermodynamic and kinetic characterization for processes involving multiple interconnected rare-event transitions remains a central challenge in molecular biophysics. This challenge is amplified when the process must be understood under a range of reaction conditions. Herein, we present a novel condition-responsive kinetic modeling framework that can combine the strengths of bottom-up rate quantification from multiscale simulations with top-down solution refinement using both equilibrium and nonequilibrium experimental data. Although this framework can be applied to any process, we demonstrate its use for electrochemically driven transport through channels and transporters via the development of electrochemically responsive rates. Using the Cl^-/H⁺ antiporter ClC-ec1 as a model system, we show how optimal and predictive kinetic solutions can be obtained when the solution space is grounded by thermodynamic constraints, seeded through multiscale rate quantification, and further refined with experimental data, such as electrophysiology assays. Turning to the Shaker K⁺ channel, we demonstrate that optimal solutions and biophysical insights can also be obtained with sufficient experimental data. This multi-pathway method also proves capable of identifying single-pathway dominant channel mechanisms but reveals that competing and off-pathway flux is still essential to replicate experimental findings and to describe concentration-dependent channel rectification.

Crystal structure prediction (CSP) seeks to identify all thermodynamically accessible solid forms of a given compound and, crucially, to establish the relative thermodynamic stability between different polymorphs. The conventional hierarchical CSP workflow suggests that no single energy model can fulfill the needs of all stages in the workflow, and energy models across a spectrum of fidelities and computational costs are required. Hybrid ab initio/empirical force-field (HAIEFF) models have demonstrated a good balance of these two factors, but the force-field component presents a major bottleneck for model accuracy. Existing parameter estimation tools for fitting this empirical component are inefficient and have severe limitations on the manageable problem size. This, combined with a lack of reliable reference data for parameter fitting, has resulted in development in the force-field component of HAIEFF models having mostly stagnated. In this work, we address these barriers to progress. First, we introduce a curated database of 755 organic crystal structures, obtained using high quality, solid-state DFT-D calculations, which provide a complete set of geometry and energy data. Comparisons to various theoretical and experimental data sources indicate that this database provides suitable diversity for parameter fitting. In tandem, we also put forward a new parameter estimation algorithm implemented as the CrystalEstimator program. Our tests demonstrate that CrystalEstimator is capable of efficiently handling large-scale parameter estimation problems, simultaneously fitting as many as 62 model parameters based on data from 445 structures. This problem size far exceeds any previously reported works related to CSP force-field parametrization. These developments form a strong foundation for all future work involving parameter estimation of transferable or tailor-made force-fields for HAIEFF models. This ultimately opens the way for significant improvements in the accuracy achieved by the HAIEFF models.

A multiscale model based on the coupling of the multiconfigurational self-consistent field (MCSCF) method and the classical atomistic polarizable fluctuating charges (FQ) force field is presented. The resulting MCSCF/FQ approach is validated by exploiting the CASSCF scheme through application to compute vertical excitation energies of formaldehyde and para-nitroaniline in aqueous solution. The procedure is integrated with molecular dynamics simulations to capture the solute's conformational changes and the dynamic aspects of solvation. Comparative analysis with alternative solvent models, gas-phase calculations, and experimental data provides insights into the model's accuracy in reproducing solute-solvent molecular interactions and spectral signals.

Geometry optimization is an important tool used for computational simulations in the fields of chemistry, physics, and material science. Developing more efficient and reliable algorithms to reduce the number of force evaluations would lead to accelerated computational modeling and materials discovery. Here, we present a delta method-based neural network-density functional theory (DFT) hybrid optimizer to improve the computational efficiency of geometry optimization. Compared to previous active learning approaches, our algorithm adds two key features: a modified delta method incorporating force information to enhance efficiency in uncertainty estimation, and a quasi-Newton approach based upon a Hessian matrix calculated from the neural network; the later improving stability of optimization near critical points. We benchmarked our optimizer against commonly used optimization algorithms using systems including bulk metal, metal surface, metal hydride, and an oxide cluster. The results demonstrate that our optimizer effectively reduces the number of DFT force calls by 2-3 times in all test systems.

A gradual but steady tide in theoretical chemistry is favoring the exploration of atomic and molecular interactions through the dynamical forces perceived and exerted by the particles of a system. By integrating the quantum mechanical force operator over all the spin and all but one of the spatial coordinates of the electrons, the Ehrenfest force density field reveals these forces directly and is separable into a classical term, related to the electric field, and a quantum mechanical correction, which we introduce and analyze for various atoms and molecules in this work. This exchange-correlation Ehrenfest force density field, F_xc(r), excludes the dominant nuclear components that shape the full Ehrenfest field, revealing information about electron sharing, pairing, and delocalization. In a manner similar, though not equal, to the electron localization function, F_xc(r) unveils covalent and core basins. Its divergence, ∇·F_xc(r), indicates the presence of electron shells in atoms and recovers the positions of lone pairs and the shell structure of ionic, polar, and covalent interactions in molecules. It also exhibits a semiquantitative match with the Laplacian of the electron density that we also explore. In alignment with the established role of exchange-correlation as nature's glue, we demonstrate that a significant number of fundamental concepts in chemical bonding can be derived from the F_xc(r) dynamical field.

Locating plausible transition paths and enhanced sampling of rare events are fundamental to understanding the functional dynamics of biomolecules. Here, a constraint-based constant advance replicas (CAR) formalism of reaction paths is reported for identifying the most probable transition path (MPTP) between two given states. We derive the temporal-integrated effective dynamics governing the projected subsystem under the holonomic CAR path constraints and show that a dynamical action functional can be defined and used for optimizing the MPTP. We further demonstrate how the CAR MPTP can be located by asymptotically minimizing an upper bound of the CAR action functional using a variational expectation-maximization framework. Essential thermodynamics and kinetic observables are retrieved by integrating the boxed molecular dynamics on the CAR MPTP using a newly proposed adaptive reflecting boundary condition. The efficiency of the proposed method is demonstrated for the Müller potential, the alanine dipeptide isomerization, and the DNA base pairing transition (Watson-Crick to Hoogsteen) in explicit solvent. The CAR representation of transition paths constitutes a robust and extensible platform that can be combined with diverse enhanced sampling methods to aid future flexible and reliable biomolecular simulations.