Journal of Chemical Theory and Computation最新文献

The success of Kohn-Sham density functional theory in predicting electronic properties from first-principles is key to its ubiquitous presence in condensed matter research. Central to this theory is the exchange-correlation functional, which can only be written in an approximate form using a handful of exact constraints. A recent criticism of these approximations is that they are designed to give an accurate description of the energy at the expense of a poor representation of the density, which is contrary to the spirit of density functional theory. These conclusions are drawn from studies of atoms or small molecules, where exact results are available. To shed light on this issue, we use the almost exact densities and energies of three prototypical solids (a semiconductor, silicon, an insulator, sodium chloride, and a metal, copper) to compare the performance of exchange-correlation functionals from all rungs of Jacob's ladder. By examining their errors in reproducing both energy and density, we show that several hybrids and semilocal functionals perform consistently well. Furthermore, functionals built to reproduce exact constraints tend to be among the top performers for all tested material classes, strengthening the argument for using these constraints in functional construction. On average, functionals published up to the early 2000s simultaneously improve the prediction of both densities and energies. This is often not the case for more recent functionals, although errors in energy and density continue to evolve in a correlated manner.

Coarse-grained molecular dynamics (CG-MD) simulations and subsequent persistent homology (PH) analysis were performed to correlate the structure and stress-strain behavior of polymer films. During uniaxial tensile MD simulations, the first principal component of the persistence diagram obtained by principal component analysis (PCA) was in good agreement with the stress-strain curve. This indicates that PH + PCA can identify critical ring structures relevant to the dynamic changes in MD simulations without requiring any prior knowledge. Inverse analysis of the persistence diagram revealed that smaller rings with ten or fewer CG beads mainly contribute to changes in the first principal component of the persistence diagram. This is due to the properties of the poly(ethylene oxide) chain, which favors the formation of a seven-membered helical structure during the self-entanglement process. The PH + PCA approach successfully reproduced the stress-strain curves for polymers with different nonbonding interactions and bond lengths. Moreover, the changes in the yield stress of each polymer film were qualitatively explained by the ring distribution in the persistence diagram. These results suggest that persistent homology analysis followed by PCA provides a versatile and powerful framework for correlating structural features with physical properties, such as ring distribution and stress-strain behavior in polymer films.

Appropriate treatment of water contributions to protein-ligand interactions is a very challenging problem in the context of adequately determining the number of waters to investigate and undertaking conformational sampling of the ligands, the waters, and the surrounding protein. In the present study, an extension of the Site Identification by Ligand Competitive Saturation-Monte Carlo (SILCS-MC) docking approach is presented that enables the determination of the location of water molecules in the binding pocket and their impact on the predicted ligand binding orientation and affinities. The approach, termed SILCS-WATER, involves MC sampling of the ligand along with explicit water molecules in a binding site followed by selection of a subset of waters within specified energetic and distance cutoffs that contribute to ligand binding and orientation. To allow for convergence of both the water and ligand orientations, SILCS-WATER is based on just the overlap of the ligand and water with the SILCS FragMaps and the interaction energy between the waters and ligand. Results show that the SILCS-WATER methodology can capture important waters and improve ligand binding orientations. For 6 of 10 multiple ligand-protein systems, the method improved relative binding affinity prediction against experimental results, with substantial improvements in five systems, when compared to standard SILCS-MC. Improved reproduction of crystallographic ligand binding orientations is shown to be an indicator of when SILCS-WATER will yield improved binding affinity correlations. The method also identifies waters interacting with ligands that occupy unfavorable locations with respect to the protein whose displacement through the appropriate ligand modifications should improve ligand binding affinity. Results are consistent with the binding affinity being modeled as a ligand-water complex interacting with the protein. The presented approach offers new possibilities in revealing water networks and their contributions to the binding orientation and affinity of a ligand for a protein and is anticipated to be of utility for computer-aided drug design.

In recent years, automated reaction path search methods have established the concept of a reaction route network. The Reaction Space Projector (ReSPer) visualizes the potential energy hypersurface into a lower-dimensional subspace using principal coordinates. The main time-consuming process in ReSPer is calculating the structural distance matrix, making it impractical for complex organic reaction route networks. We implemented the Alternate Optimization (AO) algorithm, one of the combinatorial optimizations, in ReSPer to reduce computational costs. Evaluations using gold clusters and the Au₅ several reaction route networks showed that ReSPer-AO accurately computes distances with lower computational costs. Applying ReSPer-AO to the C₅H₈O reaction route network clarified dynamic conformation changes in its potential energy landscape. The ReSPer-AO method enables analysis of chemical reactions and dynamic conformations in a low-dimensional reaction space that accurately represents hydrocarbon reaction route networks.

The Jahn-Teller (JT) effect, as a spontaneous symmetry-breaking mechanism arising from the coupling between electronic and nuclear degrees of freedom, is a widespread phenomenon in molecular and condensed matter systems. Here, we investigate the influence of the JT effect on the photodissociation dynamics of CF₃I molecules. Based on ab initio calculation, we obtain the three-dimensional potential energy surfaces for ³Q₀₊ and ¹Q₁ states and establish a diabatic Hamiltonian model to study the wavepacket dynamics in the CF₃I photodissociation process. Using the wave function of the final state after dissociation, we calculate the rotational density matrix of the CF₃ fragment and analyze its rotational excitation under the JT effect, as well as its partial coherence property and selection rules. Our work paves the way to the experimental observation and quantification of the JT effect in molecular dissociation dynamics beyond the classical ball-and-stick model.

Many quantum algorithms rely on a quality initial state for optimal performance. Preparing an initial state for specific applications can considerably reduce the cost of probabilistic algorithms such as the well studied quantum phase estimation (QPE). Fortunately, in the application space of quantum chemistry, generating approximate wave functions for molecular systems is well studied, and quantum computing algorithms stand to benefit from importing these classical methods directly into a quantum circuit. In this work, we propose a state preparation method based on coupled cluster (CC) theory, which is a pillar of quantum chemistry on classical computers, by incorporating midcircuit measurements into the circuit construction. Currently, the most well studied state preparation method for quantum chemistry on quantum computers is the variational quantum eigensolver (VQE) with a unitary-CC with single- and double-electron excitation terms (UCCSD) ansatz whose operations are limited to unitary gates. We verify the accuracy of our state preparation protocol using midcircuit measurements by performing energy evaluation and state overlap computation for a set of small chemical systems. We further demonstrate that our approach leads to a reduction of the classical computation overhead, and the number of CNOT and T gates by 28 and 57% on average when compared against the standard VQE-UCCSD protocol.

One ambimodal transition state can lead to the formation of multiple products. However, it remains fundamentally unknown how the energy and entropy along the post-TS pathways mediate ambimodal selectivity. Here, we investigated the energy and entropy profiles along the post-TS pathways in four [4 + 2]/[6 + 4] cycloadditions. We observe that the pathway leading to the minor product involves a more pronounced entropic trap. These entropic traps, resulting from the conformational change in the dynamic course of ring closure, act as a reservoir of longer-lived dynamic intermediates that roam on the potential energy surface and have a higher likelihood of redistributing to form the other product. The SpnF-catalyzed Diels-Alder reaction produces [4 + 2] and [6 + 4] adducts with nearly equal product distribution and relatively flat energy profiles, in contrast to other cycloadditions. Unexpectedly, the entropy profiles for these two adducts are distinctly different. The formation of the [6 + 4] adduct encounters an entropic barrier acting as a dynamical bottleneck, while the [4 + 2] adduct involves a substantial entropic trap to maintain long-lived intermediates. These opposing effects hinder both product formations and likely cancel each other out so that an equal product distribution is observed.

Methods for calculating the relative binding free energy (RBFE) between ligands to a target protein are gaining importance in the structure-based drug discovery domain, especially as methodological advances and automation improve accuracy and ease of use. In an RBFE calculation, the difference between the binding affinities of two ligands to a protein is calculated by transforming one ligand into another, in the protein-ligand complex, and in solvent. Alchemical binding free energy calculations are often used for such ligand transformations. Such calculations are not without challenges, however; for example, it can be challenging to handle interfacial waters when these play a crucial role in mediating protein-ligand binding. In some cases, the exchange of the interfacial waters with solvent water might be very infrequent in the course of typical molecular simulations, and such interfacial waters can be considered trapped on the simulation time scale. In these cases, RBFE calculation between two ligands, where one ligand binds with a trapped water while the other ligand displaces it, can result in inaccuracies if the surrounding water structure is not sampled adequately for both ligands. So far, a popular choice for treating the trapped waters in RBFE calculations is to combine free energy calculations with enhanced sampling methods that insert/delete waters in the binding site. Despite recent developments in the enhanced sampling methods, they can result in hysteresis in the RBFE estimate, depending on whether the simulations were started with or without the trapped waters. In this study, we introduce an alternative method, separation of states, to calculate the RBFE between ligand pairs where the ligands bind to the protein with different numbers/positions of trapped waters. The separation of states approach treats the sampling of the trapped waters separately from the free energy calculation of the ligand transformation. In our method, a trapped water in protein's binding site is decoupled from the system first, and the cavity created by its decoupling is stabilized. We then grow a larger ligand into this cavity- a ligand that is known to displace the trapped water. In this study, we show that our method results in precise and accurate estimates of RBFEs for ligand pairs involving the rearrangement of trapped water via RBFE calculations for five such ligand pairs. We have optimized our simulation protocol to be suited for large distributed computational resources and have automated our RBFE calculation workflow.

Global potential energy surface (PES) exploration provides a unique route to predict the thermodynamic and kinetic properties of unknown materials, but the task is highly challenging for systems with tight covalent bonds. Here, we develop the local-softening stochastic surface walking (LS-SSW) method for scanning corrugated PESs. LS-SSW transforms the vibrational mode space of a system by adding pairwise penalty potentials with a self-adaption mechanism, which helps to delocalize and soften the strong local modes. This allows the stochastic surface walking (SSW) method to capture more efficiently the correct local atomic movement toward nearby minima and simultaneously reduce the barrier height of reactions. As a result, the local trapping time in searching for the corrugated PES is greatly reduced. LS-SSW can be applied generally to the reaction pathway sampling and the global PES exploration of both clusters and crystals, the high efficiency of which is demonstrated in searching the reaction pathways between C₄H₆ isomers, finding the global minimum of carbon clusters up to 360 atoms, and constructing the global PES of Fe₇C₃ material.

Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power and theoretical methods. This paper introduces a novel, cloud-native application, Accelerated DFT, which offers an order of magnitude acceleration in DFT simulations. By integrating state-of-the-art cloud infrastructure and redesigning algorithms for graphic processing units (GPUs), Accelerated DFT achieves high-speed calculations without sacrificing accuracy. It provides a user-friendly and scalable solution for the increasing demands of DFT calculations in scientific communities. The implementation details, examples, and benchmark results illustrate how Accelerated DFT can significantly expedite scientific discovery across various domains.