Journal of Chemical Theory and Computation最新文献

Relativistic pseudopotentials (PPs) and basis sets are the workhorses for modeling heavy elements of lanthanides and actinides. The norm-conserving Goedecker-Teter-Hutter (GTH) PP is advantageous for modeling lanthanide and actinide compounds and condensed systems because of its transferability and accuracy. In this work, we develop a set of well-benchmarked GTH-type 5f-in-core PPs with scalar-relativistic effects together with associated Gaussian basis sets for the most commonly encountered trivalent and tetravalent actinides [An(III), An(IV); An = Pa-Lr]. The 5f-in-core GTH PPs are constructed by placing 5f-subconfiguration 5fⁿ of An(III) and 5f^n-1 of An(IV) (n = 2-14) into the atomic core in the core-valence separation. The formalism of 5f-in-core GTH PPs circumvents the computational difficulty arising from the 5f open valence shell. The different performances of 5f-in-core GTH PPs for trivalent and tetravalent actinides are further analyzed from the chemical bonding features of actinides. We anticipate that the optimized 5f-in-core GTH PPs and Gaussian basis sets can be used to accelerate the costly first-principles modeling of structure-complicated actinide compounds and condensed-phase actinide systems.

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.

Due to their computational intensity, long-range corrections of three-body interactions are particularly desirable, while there is no consensus of how to devise a cutoff scheme. A cutoff correction scheme for three-body interactions in molecular simulations is proposed that does not rest on complex integrals and can be implemented straightforwardly. For a limited number of configurations, the three-body interactions are evaluated for a desired and a very large cutoff radius to determine the required corrections.

Inductive program synthesis (PS) has recently begun to emerge as a useful new approach to automatically generate algorithms in quantum chemistry, as demonstrated in recent applications to the vibrational Schrödinger equation for simple model systems with one or two degrees-of-freedom. Here, we report a new physics-informed approach to inductive PS that is more conducive to the generation of discrete variable representation algorithms for real molecular systems. The new framework ensures separability of the kinetic and potential operators and does not require an exact solution to compare synthesized algorithmic predictions with. Algorithms with a tridiagonal matrix structure are generated via a variational-based stochastic optimization procedure. Crucially, through an extensive testing procedure, we demonstrate that variationally synthesized algorithms perform just as well as those generated using a target function. Assuming a direct product representation of normal coordinates, these algorithms are applied to three triatomic molecules. In total, we identify a set of seven PS algorithms that accurately reproduce the vibrational spectra of H₂O, NO₂, and SO₂, as predicted by Colbert-Miller and sine-DVR algorithms.

We present the implementation of the time-domain multichromophoric fluorescence resonant energy transfer (TC-MCFRET) approach in the numerical integration of the Schrödinger equation (NISE) program. This method enables the efficient simulation of incoherent energy transfer between distinct segments within large and complex molecular systems, such as photosynthetic complexes. Our approach incorporates a segmentation protocol to divide these systems into manageable components and a modified thermal correction to ensure detailed balance. The implementation allows us to calculate the energy transfer rate in the NISE program systematically and easily. To validate our method, we applied it to a range of test cases, including parallel linear aggregates and biologically relevant systems like the B850 rings from LH2 and the Fenna-Matthews-Olson complex. Our results show excellent agreement with previous studies, demonstrating the accuracy and efficiency of our TD-MCFRET method. We anticipate that this approach will be widely applicable to the calculation of energy transfer rates in other large molecular systems and will pave the way for future simulations of multidimensional electronic spectra.

Adsorbate free energies are important parameters in surface chemistry and catalysis. Because of its simplicity, the harmonic oscillator (HO) model remains the most widely used method for calculating adsorbate free energy in many fields, including microkinetic modeling. However, it is well-known that the HO method is ineffective for weak adsorption. In this study, we propose a translational model with a diffusion barrier to calculate the state functions of near free translation. Furthermore, an effective mass is introduced in this model. To address hindered translation uniformly, a diffusion barrier-based damping function (DF) is proposed that effectively links the harmonic vibration and translation limits. Adsorbates are divided into three categories according to their adsorption strength and diffusion barrier height. Adsorbed hydrogen atoms have a strong binding energy and relatively high vibrational frequency but a low diffusion barrier. The HT and our proposed DF methods predict that the adsorbed hydrogen atoms behave as translation above room temperature, while the previous DF method predicts that they behave as vibration at any temperature. At last, the dehydrogenation reaction of propane on the Pt(111) surface was taken as an example to illustrate the influence of different methods on the thermodynamic functions.

Density functional theory has become the workhorse of quantum physics, chemistry, and materials science. Within these fields, a broad range of applications needs to be covered. These applications range from solids to molecular systems, from organic to inorganic chemistry, or even from electrons to other Fermions, such as protons or muons. This is emphasized by the plethora of density functional approximations that have been developed for various cases. In this work, two new local hybrid exchange-correlation density functionals are constructed from first-principles, promoting generality and transferability. We show that constraint satisfaction can be achieved even for admixtures with full exact exchange, without sacrificing accuracy. The performance of the new functionals CHYF-PBE and CHYF-B95 is assessed for thermochemical properties, excitation energies, Mössbauer isomer shifts, NMR spin-spin coupling constants, NMR shieldings and shifts, magnetizabilities, and EPR hyperfine coupling constants. Here, the new density functional shows excellent performance throughout all tests and is numerically robust only requiring small grids for converged results. Additionally, both functionals can easily be generalized to arbitrary Fermions as shown for electron-proton correlation energies. Therefore, we outline that density functionals generated in this way are general purpose tools for quantum mechanical studies.

Helices are important secondary structural motifs within proteins and are pivotal in numerous physiological processes. While amino acids (AA) such as alanine and leucine are known to promote helix formation, proline and glycine disfavor it. Helical structure formation, however, also depends on its environment, and hence, prior prediction of a mutational effect on a helical structure is difficult. Here, we employ a reinforcement learning algorithm to develop a predictive model for helix-disrupting mutations. We start with a model to disrupt helices independent of their protein environment. Our results show that only a few mutations lead to a drastic disruption of the target helix. We further extend our approach to helices in proteins and validate the results using rigorous free energy calculations. Our strategy identifies amino acids crucial for maintaining structural integrity and predicts key mutations that could alter protein structure. Through our work, we present a new use case for reinforcement learning in protein structure disruption.

Quantities calculated from molecular simulations are often subject to an initial bias due to unrepresentative starting configurations. Initial data are usually discarded to reduce bias. Chodera's method for automated truncation point selection [J. Chem. Theory Comput. 2016, 12, 4, 1799-1805] is popular but has not been thoroughly assessed. We reformulate White's marginal standard error rule to provide a spectrum of truncation point selection heuristics that differ in their treatment of autocorrelation. These include a method effectively equivalent to Chodera's. We test these methods on ensembles of synthetic time series modeled on free energy change estimates from long absolute binding free energy calculations. Methods that more thoroughly account for autocorrelation often show late and variable truncation times, while methods that less thoroughly account for autocorrelation often show early truncation, relative to the optimal truncation point. This increases variance and bias, respectively. We recommend a method that achieves robust performance across our test sets by balancing these two extremes. None of the methods reliably detected insufficient sampling. All heuristics tested are implemented in the open-source Python package RED (github.com/fjclark/red).

The efficient generation of complex initial structures for polymers remains a critical challenge in the field of molecular simulation. This necessitates the development of high-quality and highly efficient modeling algorithms. Inspired by fundamental polymerization reactions, we propose a general algorithm for an efficient de novo polymer model building, resulting in the development of the eXtendable Polymer Builder (XPB) package. We show that XPB is well-suited for constructing a wide range of polymer models, including linear, dendritic, and cross-linked structures. It offers a precise control over polymer morphology through adjustable, physically meaningful parameters such as residue types, connection preferences, and cross-linking distances. As a showcase, XPB can construct well-defined dendrimers up to the 10th generation and hyperbranched polymers with tens of thousands of residues within mere minutes, while effectively minimizing structural overlaps. This versatility facilitates the construction of more complex polymer architectures than before, providing a general and robust framework for the high-throughput and high-quality generation of diverse polymer structures.