Journal of Chemical Theory and Computation最新文献

Hybrid quantum-classical computing algorithms offer significant potential for accelerating the calculation of the electronic structure of strongly correlated molecules. In this work, we present the first quantum simulation of conical intersections (CIs) in a biomolecule, cytosine, using a superconducting quantum computer. We apply the contracted quantum eigensolver (CQE)─with comparisons to conventional variational quantum deflation (VQD)─to compute the near-degenerate ground and excited states associated with the conical intersection, a key feature governing the photostability of DNA and RNA. The CQE is based on an exact ansatz for many-electron molecules in the absence of noise─a critically important property for resolving strongly correlated states at CIs. Both methods demonstrate promising accuracy when compared with exact diagonalization, even on noisy intermediate-scale quantum computers, highlighting their potential for advancing the understanding of photochemical and photobiological processes. The ability to simulate these intersections is critical for advancing our knowledge of biological processes like DNA repair and mutation, with potential implications for molecular biology and medical research.

We present the theory, implementation, and benchmarking of a real-time time-dependent density functional theory (RT-TDDFT) module within the RMG code, designed to simulate the electronic response of molecular systems to external perturbations. Our method offers insights into nonequilibrium dynamics and excited states across a diverse range of systems, from small organic molecules to large metallic nanoparticles. Benchmarking results demonstrate excellent agreement with established TDDFT implementations and showcase the superior stability of our time integration algorithm, enabling long-term simulations with minimal energy drift. The scalability and efficiency of RMG on massively parallel architectures allow for simulations of complex systems, such as plasmonic nanoparticles with thousands of atoms. Future extensions, including nuclear and spin dynamics, will broaden the applicability of this RT-TDDFT implementation, providing a powerful toolset for studies of photoactive materials, nanoscale devices, and other systems where real-time electronic dynamics is essential.

Most state-of-the-art neural network potentials do not account for molecular attributes other than atomic numbers and positions, which limits its range of applicability by design. In this work, we demonstrate the importance of including additional electronic attributes in neural network potential representations with a minimal architectural change to TensorNet, a state-of-the-art equivariant model based on Cartesian rank-2 tensor representations. By performing experiments on both custom-made and public benchmarking data sets, we show that this modification resolves input degeneracy issues stemming from the use of atomic numbers and positions alone, while enhancing the model's predictive accuracy across diverse chemical systems with different charge or spin states. This is accomplished without tailored strategies or the inclusion of physics-based energy terms, while maintaining efficiency and accuracy. These findings should furthermore encourage researchers to train and use models incorporating these additional representations.

In this review we discuss the development of methodology for calculating the temperature dependence and thermodynamic activation parameters for chemical reactions in solution and in enzymes, from computer simulations. We outline how this is done by combining the empirical valence bond method with molecular dynamics free energy simulations. In favorable cases it turns out that such simulations can even capture temperature optima for the catalytic rate. The approach turns out be very useful both for addressing questions regarding the roles of enthalpic and entropic effects in specific enzymes and also for attacking evolutionary problems regarding enzyme adaptation to different temperature regimes. In the latter case, we focus on cold-adaptation of enzymes from psychrophilic species and show how computer simulations have revealed the basic mechanisms behind such adaptation. Understanding these mechanisms also opens up the possibility of designing the temperature dependence, and we highlight a recent example of this.

An approach to simulate nonadiabatic dynamics in solution is introduced, which relies on the propagation of the nuclear wavepacket with the Ab Initio Multiple Spawning (AIMS) method under the effect of potential energy calculated with a hybrid but fully quantum mechanical scheme (QM/QM'). The electronic energies of the excited states of the chromophore are calculated with multireference perturbation theory (CASPT2), and the embedding molecules are described with a tight binding Hamiltonian (GFN2-xTB). This implementation is fully open source and relies on the combination of PySpawn, OpenMolcas, and xTB. Additionally, ORCA is used to properly generate the initial conditions in solution, showing how the combination of cutting-edge implementations in several commonly used software can push the state of the art of nonadiabatic dynamics in solution toward a new high standard of accuracy. The dynamics of ethylene in vacuum, in acetone, and in chloroform is reported as a test case, with a detailed analysis of the AIMS runs that shows important geometrical and electronic effects of the solvents on the decay mechanism of the chromophore.

A dispersion-corrected density functional theory (DFT+D) method has been developed. It includes a nonhybrid dispersionless generalized gradient approximation (GGA) functional paired with a literature-parametrized dispersion function. The functional's 9 adjustable parameters were optimized using a training set of 589 benchmark interaction energies. The resulting method performs better than other GGA-based DFT+D methods, giving a mean unsigned error of 0.33 kcal/mol. It even performs better than some more expensive meta-GGA or hybrid dispersion-corrected functionals. An important advantage of using the new functional is that its dispersion energy given by the D component is very close to the true dispersion energy at all intermolecular separations, whereas in other similarly accurate DFT+D approaches, such a dispersion contribution in the van der Waals minimum region is only a small fraction of the true value.

Accurately calculating the diradical character (y₀) of molecular systems remains a significant challenge due to the scarcity of experimental data and the inherent multireference nature of the electronic structure. In this study, various quantum mechanical approaches, including broken symmetry density functional theory (BS-DFT), spin-flip time-dependent density functional theory (SF-TDDFT), mixed-reference spin-flip time-dependent density functional theory (MRSF-TDDFT), complete active space self-consistent field (CASSCF), complete active space second-order perturbation theory (CASPT2), and multiconfigurational pair-density functional theory (MCPDFT), are employed to compute the singlet-triplet energy gaps (E_ST) and y₀ values in Thiele, Chichibabin, and Müller analogous diradicals. By systematically comparing the results from these computational methods, we identify optimally tuned long-range corrected functional CAM-B3LYP in the BS-DFT framework as a most efficient method for accurately and affordably predicting both E_ST and y₀ values. Additionally, our results demonstrate that (i) MRSF-TDDFT performs much better than SF-TDDFT; (ii) the MCPDFT method is robust in determining E_ST with minimal dependence on the choice of active space. These findings provide insight into the electronic structure and diradical character of the investigated molecules and highlight effective computational strategies for future studies in this domain. Thus, this work not only advances our understanding of diradical systems but also offers practical guidelines for their computational investigation.

Molecular docking is a crucial technique for elucidating protein-ligand interactions. Machine learning-based docking methods offer promising advantages over traditional approaches, with significant potential for further development. However, many current machine learning-based methods face challenges in ensuring the physical plausibility of generated docking poses. Additionally, accommodating protein flexibility remains difficult for existing methods, limiting their effectiveness in real-world scenarios. Herein, we present ApoDock, a modular docking paradigm that combines machine learning-driven conditional side chain packing based on the protein backbone and ligand information with traditional sampling methods to ensure physically realistic poses. The generated poses are finally scored by the developed mixture density network-based scoring function. With accurate side chain packing, physical-based pose sampling, and accurate pose ranking ability, ApoDock demonstrates competitive performance across diverse applications, especially when using modeled structure (AlphaFold2 and ESMFold) for docking, exhibiting a success rate of 28.5% higher than that of other state of the art (SOTA), highlighting its potential as a valuable tool for protein-ligand binding studies and related applications.

In this work, we performed a theoretical calculation of the negative ion conversion by a neutral carbon atom beam grazing scattering from the KI(100) surface. The Madelung potential, image potential, and ML-polarization interaction contributions are included in the calculation of the electron capture energy defect of the valence band near surface anion sites along the projectile incidence direction. The loss of the formed negative ions does not originate from the electron loss to the unoccupied conduction band or neutral exciton states but results from the Coulomb barrier tunneling detachment of the loosely bound affinity electron to the vacuum level during the interaction with surface lattice anion sites. Here, the large fraction of negative-ion conversion (≥50%) within the projectile energy range of E_p ∈ [5.7,14.9] keV implies the present collision system could be directly used to design the next-generation negative carbon ion sources for the study of isotope shifts in electron affinity, electron correlation effects and for the promotion of nerve tissue repair and regeneration by negative carbon ion irradiation.

Nonadiabatic coupling between electrons and molecular motion at metal surfaces leads to energy dissipation and dynamic steering effects during chemical surface dynamics. We present a theoretical approach to the scattering of molecules from metal surfaces that incorporates all nonadiabatic and quantum nuclear effects due to the coupling of the molecular degrees of freedom to the electrons in the metal. This is achieved with the hierarchical equations of motion (HEOM) approach, combined with a matrix product state representation in twin space. The method is applied to the scattering of nitric oxide from Au(111), for which strongly nonadiabatic energy loss during scattering has been experimentally observed, thus presenting a significant theoretical challenge. Since the HEOM approach treats the molecule-surface coupling exactly, it captures the interplay between nonadiabatic and quantum nuclear effects. Finally, the data obtained by the HEOM approach are used as a rigorous benchmark to assess various mixed quantum-classical methods, from which we derive insights into the mechanisms of energy dissipation and the suitable working regimes of each method.