Journal of Chemical Theory and Computation最新文献

Singlet fission is a photophysical process in organic molecules that generates two triplet electronic states from an excited singlet electronic state. Molecules exhibiting singlet fission can multiply charge carriers and thus have the potential to enhance the performance of solar cells beyond the Shockley–Queisser limit by reducing thermalization losses. However, in order to implement singlet fission for applications in photovoltaics, it is essential to understand how charge or energy can be harvested from triplet excitons. In this work, we investigate these processes in a prototypical donor–acceptor complex consisting of a bis(diazadiborine)-based chromophore as a singlet fission-active donor and tetracyanoquinodimethane as an acceptor molecule. Using a combined approach of high-level ab initio multireference perturbation theory techniques and quantum dynamical simulations, we show the existence of intermolecular singlet fission, charge and energy transfer following intramolecular singlet fission, and energy loss decay channels to low-lying states as the three competing charge and energy transfer mechanisms from the donor to the acceptor molecule. We analyze the role of the different electronic states, specific vibrational modes, and vibronic couplings in these processes. The results provide insights into the rational design of donor–acceptor systems with efficient singlet fission-based charge and energy transfer.

Introducing electric fields into density functional theory (DFT) calculations is essential for understanding electrochemical processes, interfacial phenomena, and the behavior of materials under applied bias. However, applying user-defined electrostatic potentials in DFT is nontrivial and often requires direct modification to the specific DFT code. In this work, we present an implementation for supercell DFT calculations under arbitrary electric fields and discuss the required corrections to the energies and forces. The implementation is realized through the recently released VASP–Python interface, enabling the application of user-defined fields directly within the standard VASP software and providing great flexibility and control. We demonstrate the application of this approach with diverse case studies, including molecular adsorption on electrified surfaces, field ion microscopy, electrochemical solid–water interfaces, and implicit solvent models.

The efficacy and equitable distribution of viral biologics, including vaccines and virus-like particles, are hindered due to their inherently low shelf life. To increase the longevity of such products, formulations are typically developed with small molecule additives known as excipients. Finding the correct excipients for a biological formulation is a costly and time-consuming process due to the large excipient design space and the unknown mechanisms underlying excipient–virus interactions. Molecular dynamics simulations are, in theory, well-equipped to efficiently investigate these mechanisms. However, the massive size of fully assembled viral capsids, the protein shell that encapsulates the viral genome, demands computational resources well beyond the requirements of conventional simulations. There exists a need for a novel method that enables high-throughput investigations of virus–excipient interactions at the molecular level and at atomistic resolution. Here, we introduce CapSACIN─a computational framework for Capsid Surface Abstraction and Computationally-Induced Nanofragmentation. We demonstrate the applicability of this workflow to a model nonenveloped virus, porcine parvovirus (PPV). Through simulations of PPV surface models, we observe that the 2-fold axis of symmetry is significantly weaker at the molecular level than the 3- or 5-fold axes of symmetry. Further, we present results demonstrating excellent agreement with experimentally determined excipient effects on PPV thermal stability.

In this work, we present a novel computational workflow for accelerating the identification of percolation pathways in twisted bilayer graphene of nearly point-charge ions. The method uses the charge density from a single ab initio calculation using Density Functional Theory and requires that the percolating ion only weakly influences the charge density of the host material. The method is composed of three steps. First the intercalation sites in the bilayer are identified, then a graph describing the possible migrations between those is generated, and last a path-finding algorithm is used to discover the lowest-cost percolation paths. We have applied this workflow to Li-diffusion in 21 different twist-angle structures of twisted bilayer graphene, which could be imagined as a potential anode material in Li-ion batteries. We found that it yields physically plausible pathways in all examined cases and observed a significant relationship between the twist angle and the ease-of-percolation, highlighting the value of computational studies in mapping percolation paths. Our method is general and much faster than that conventionally used to determine percolation paths. Therefore, the method enables the efficient investigation of percolation pathways in diverse materials, including other 2D heterostructures and even 3D crystalline materials with trivial alterations.

We present a molecular dynamics simulation study of the E. coli ribose transporter protein B (RbsB), a conformationally labile protein found in the periplasm of the bacterium. The ribose transporter exhibits characteristics of both traditional type I and type II import systems. In our study, we observed the full conformational transition of the periplasmic binding protein RbsB for the first time. Our study revealed that in most scenarios (all but one) the conformational changes preceded the departure of ribose from the binding site, a process likely influenced by specific interactions at the binding interface. Indeed, our analyses of ribose binding revealed that specific salt bridges played a crucial role in stabilizing the closed conformation of RbsB. Our simulations also provided further evidence for a putative structural water molecule, which had also been observed from X-ray data. Crucially, our simulations were run with three different force fields: CHARMM36(m), AMBER ff19SB, and CHARMM36(m) with SIRAH coarse-grained water. This strategy enabled us to observe all of the conformational states that had been identified in structural studies. Thus, we argue that the subtle biases of individual force fields can be utilized to enhance conformational sampling.

In this work, we present a theoretical and computational approach that combines real-time propagation of the electronic wave function, the GW/BSE formalism for the electronic structure of ground and excited states, the theory of open quantum systems, and the phase-cycling method to compute two-dimensional electronic spectra (2DES) of molecular systems under realistic excitation conditions. The advantage of this strategy is that it combines the accuracy of first-principle calculations such as GW/BSE with an explicit description of the employed laser pulses. This allows for better adherence to experimental setups. We apply the proposed methodology to benzene, chlorophyll b, and a benzene–phenol dimer, also including a pure electronic dephasing in the time propagation. The calculated 2DES maps reveal clear signatures of stimulated emission and excited-state absorption, as well as coherence dynamics as a function of the population time, both in the absence and presence of pure dephasing. Comparison with experimental and theoretical published data has been carried out, when available.

A potential energy curve (PEC) accurate to a fraction of 1 ppm (1:10⁶) is computed for the a ³Σ_u⁺ state of He₂ endowed with relativistic and QED corrections. The nuclear Schrödinger equation is solved on this PEC with diagonal Born–Oppenheimer and nonadiabatic mass corrections to obtain highly accurate rotational–vibrational levels. The computed rovibrational intervals and fine-structure splittings, spanning over several orders of magnitude in energy, are found to be in remarkable agreement with available high-resolution spectroscopy data.

The Martini coarse-grained (CG) force field enables efficient simulations of biomolecular systems but cannot reliably maintain folded protein structures. To stabilize proteins during simulation, Martini is typically combined with structure-based force fields such as elastic network models (ENMs) or Go̅ models. While these approaches preserve global folds and capture protein flexibility, their ability to reproduce conformational dynamics remains unclear. Here, we evaluate Martini 3 combined with ENMs or Go̅ models on three folded proteins and show that both approaches struggle to sample the conformational space observed in atomistic simulations, even when uniform interaction strengths or equilibrium bond distances are adjusted. This limitation arises from the assumption of a uniform interaction network, in which all Go̅-bonds are assigned the same ϵ value, and therefore have the same potential depth. To overcome this, we present a fully automated, perturbation-based optimization approach for Go̅ networks, PoGo̅, that iteratively refines a nonuniform Go̅ network against a precomputed atomistic free-energy landscape in essential conformational space. Moreover, we demonstrate that our approach can also be used to optimize ENMs. In both cases, convergence is rapid and yields CG ensembles in close agreement with reference atomistic simulations. As a cross-validation, the optimization also improves the root-mean-square fluctuation profile.

We present a general framework for performing local vibrational mode analysis of vibrations in crystalline materials at arbitrary wavevectors throughout the Brillouin zone. The approach enables phonon dispersion relations to be interpreted in terms of chemically meaningful interatomic interactions and structural motifs, providing direct insight into the microscopic origins of the phonon behavior in periodic systems. We demonstrate the methodology for representative one-, two-, and three-dimensional materials including polymeric chains, graphene, and prototypical rock-salt and perovskite crystals. Across these systems, the analysis reveals how specific bonding patterns and structural features govern phonon dispersion relations. This framework provides a quantitative tool for the chemically intuitive analysis of phonon spectra and offers a pathway toward the rational design of phonon-dependent properties in crystalline materials.

Dopants can tune the performance of MoS₂ in various applications, but the use of molecular dynamics simulations for doped MoS₂ materials discovery is limited by the lack of multidopant interatomic potentials. Universal machine learning interatomic potentials (MLIPs) could be a solution, but the accuracy of these potentials must first be evaluated. Here, we evaluate the accuracy of a recently developed MLIP, META’s Universal Model for Atoms, for 25 different MoS₂ dopants spanning metals, nonmetals, and transition metals in Mo-substitution, S-substitution, and intercalated positions by benchmarking the MLIP-predicted formation energy and the dopant-induced structural change against density functional theory (DFT) calculations. The computational framework for MLIP validation and simulations is described in detail, and the source code is made available online. The MLIP is then demonstrated by performing heating–cooling simulations of MoS₂ supercells with all 25 dopants. These simulations capture complex phenomena including dopant clustering, MoS₂ layer fracturing, interlayer diffusion, and chemical compound formation at orders-of-magnitude reduced computational cost compared to DFT. This work provides a computational workflow for the application-oriented design of doped-MoS₂, enabling high-throughput screening of dopant candidates and optimization of compositions for targeted tribological, electronic, and optoelectronic performance.