Journal of Chemical Theory and Computation最新文献

We present a general scheme to improve the exchange-correlation potential of standard Kohn-Sham methods, like the PBE (Perdew, Burke, Ernzerhof) or PBE0 method, by enforcing exact conditions the exchange-correlation potential has to obey during their calculation. The required modifications of the potentials are enabled by generating the potentials within the optimized-effective-potential (OEP) framework instead of directly taking the functional derivative with respect to the electron density on a real-space grid as usual. We generalize a condition for the exact exchange potential that involves the eigenvalues of the highest occupied molecular orbital such that it is applicable to arbitrary approximate exchange potentials. The new approach yields strongly improved exchange-correlation potentials which lead to qualitatively and quantitatively improved KS orbital and eigenvalue spectra containing a Rydberg series as required and obeying much better the Kohn-Sham ionization energy theorem. If the resulting orbitals and eigenvalues are used as input quantities in time-dependent density-functional theory (TDDFT) to calculate excitation energies then the accuracy of the latter is drastically improved, e.g., for TDDFT with the PBE functional the accuracy of excitation energies is improved by a factor of roughly three. This make the introduced approach highly attractive for generating input orbitals and eigenvalues for TDDFT but potentially also for high-rung correlation functionals that are typically evaluated in a post-SCF (post self-consistent-field) manner. We apply the new approach to calculate exchange-correlation potentials to the PBE and PBE0 functionals but the approach is generally applicable to any functional.

Quantum Monte Carlo (QMC) methods are powerful approaches for solving electronic structure problems. Although they often provide high-accuracy solutions, the precision of most QMC methods is ultimately limited by the trial wave function that must be used. Recently, an approach has been demonstrated to allow the use of trial wave functions prepared on a quantum computer [Huggins et al., Unbiasing fermionic quantum Monte Carlo with a quantum computer. Nature 2022, 603, 416] in the auxiliary-field QMC (AFQMC) method using classical shadows to estimate the required overlaps. However, this approach has an exponential post-processing step to construct these overlaps when performing classical shadows obtained using random Clifford circuits. Here, we study an approach to avoid this exponential scaling step by using a fixed-node Monte Carlo method based on full configuration interaction quantum Monte Carlo. This method is applied to the local unitary cluster Jastrow ansatz. We consider H₄, ferrocene, and benzene molecules using up to 12 qubits as examples. Circuits are compiled to native gates for typical near-term architectures, and we assess the impact of circuit-level depolarizing noise on the method. We also provide a comparison of AFQMC and fixed-node approaches, demonstrating that AFQMC is more robust to errors, although extrapolations of the fixed-node energy reduce this discrepancy. Although the method can be used to reach chemical accuracy, the sampling cost to achieve this is high even for small active spaces, suggesting caution about the prospect of outperforming conventional QMC approaches.

We introduce a black-box auxiliary field quantum Monte Carlo (AFQMC) approach to perform highly accurate electronic structure calculations using configuration interaction singles and doubles (CISD) trial states. This method consistently provides more accurate energy estimates than coupled cluster singles and doubles with perturbative triples (CCSD(T)), often regarded as the gold standard in quantum chemistry. This level of precision is achieved at a lower asymptotic computational cost, scaling as O(N⁶) compared to the O(N⁷) scaling of CCSD(T). We provide numerical evidence supporting these findings through results for challenging main group and transition metal-containing molecules.

A novel formulation is presented for the treatment of electrostatics in the periodic GFN1-xTB tight-binding model. Periodic GFN1-xTB is hindered by the functional form of the second-order electrostatics, which only recovers Coulombic behavior at large interatomic distances and lacks a closed-form solution for its Fourier transform. We address this by introducing a binomial expansion of the Klopman-Ohno function to partition short- and long-range interactions, enabling the use of a generalized Ewald summation for the solution of the electrostatic energy. This approach is general and is applicable to any damped potential of the form |Rⁿ + c|^-m. Benchmarks on the X23 molecular crystal dataset and a range of prototypical bulk semiconductors demonstrate that this systematic treatment of the electrostatics eliminates unphysical behavior in the equation of state curves. In the bulk systems studied, we observe a mean absolute error in total energy of 35 meV/atom, comparable to the machine-learned universal force field, M3GNet, and sufficiently precise for structure relaxation. These results highlight the promising potential of GFN1-xTB as a universal tight-binding parametrization.

Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parametrized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multiscale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution.

Molecular exciton polaritons, hybrid states formed through the strong coupling of molecular electronic excitations with optical cavity modes, offer a powerful avenue for controlling photophysical and photochemical processes in molecular systems. Here, we present a semiclassical framework for investigating the dynamics of molecular exciton polaritons using the truncated Wigner approximation (TWA). This approach extends the prior TWA method developed for molecular vibration-polariton dynamics ( J. Chem. Theory Comput. 2024, 20, 3019-3027) by incorporating semiclassical treatment of quantum coherence between ground and excited molecular states. To validate the framework, we first apply it to a simplified system of two-level (spin-1/2) molecules without vibronic coupling, demonstrating strong agreement between semiclassical and fully quantum simulations in systems with a large molecular ensemble. We further extend the model to include molecular vibronic coupling, revealing the dynamic polaron decoupling effect, where the quantum coherence between molecular excitations persists under strong light-matter coupling. These findings provide critical insights into the collective behavior and coherence preservation in polaritonic systems with implications for designing cavity-mediated molecular processes.

While nowadays approaches for equilibrium free energy estimation are well established, nonequilibrium simulations represent both an appealing computational opportunity and a challenge. This kind of simulations allows for a trivially parallel scheme, but at the same time the significant amount of irreversible work often generated during the steering process (either alchemical or physical) can hinder the convergence of free energy estimators. Here, we discuss in depth this issue for the protein-ligand binding free energy estimation carried out via physical paths. We found that the water model and the parametrization of the path collective variables have a remarkable impact on the convergence rate of the estimators (e.g., Crooks). Finally, we provide practical recipes to enhance the convergence speed and minimize dissipation.

Most enhanced sampling methods facilitate the exploration of molecular free energy landscapes by applying a bias potential along a reduced dimensional collective variable (CV) space. The success of these methods depends on the ability of the CVs to follow the relevant slow modes of the system. Intuitive CVs, such as distances or contacts, often prove inadequate, particularly in biological systems involving many coupled degrees of freedom. Machine learning algorithms, especially neural networks (NN), can automate the process of CV discovery by combining a large number of molecular descriptors and often outperform intuitive CVs in sampling efficiency. However, their lack of interpretability and high cost of evaluation during trajectory propagation make NN-CVs difficult to apply to large biomolecular processes. Here, we introduce a surrogate model approach using lasso regression to express the output of a neural network as a linear combination of an automatically chosen subset of the input descriptors. We demonstrate successful applications of our surrogate model CVs in the enhanced sampling simulation of the conformational landscape of alanine dipeptide and chignolin mini-protein. In addition to providing mechanistic insights due to their explainable nature, the surrogate model CVs showed a negligible loss in efficiency and accuracy, compared to the NN-CVs, in reconstructing the underlying free energy surface. Moreover, due to their simplified functional forms, these CVs are better at extrapolating to unseen regions of the conformational space, e.g., saddle points. Surrogate model CVs are also less expensive to evaluate compared to their NN counterparts, making them suitable for enhanced sampling simulation of large and complex biomolecular processes.

Excited-state molecular dynamics (ESMD) simulations near conical intersections (CIs) pose significant challenges when using machine learning potentials (MLPs). Although MLPs have gained recognition for their integration into mixed quantum-classical (MQC) methods, such as trajectory surface hopping (TSH), and their capacity to model correlated electron-nuclear dynamics efficiently, difficulties persist in managing nonadiabatic dynamics. Specifically, singularities at CIs and double-valued coupling elements result in discontinuities that disrupt the smoothness of predictive functions. Partial solutions have been provided by learning diabatic Hamiltonians with phaseless loss functions to these challenges. However, a definitive method for addressing the discontinuities caused by CIs and double-valued coupling elements has yet to be developed. Here, we introduce the phaseless coupling term, Δ², derived from the square of the off-diagonal elements of the diabatic Hamiltonian in the state-interaction state-averaged spin-restricted ensemble-referenced Kohn-Sham (SI-SA-REKS, briefly SSR)(2,2) formalism. This approach improves the stability and accuracy of the MLP model by addressing the issues arising from CI singularities and double-valued coupling functions. We apply this method to the penta-2,4-dieniminium cation (PSB3), demonstrating its effectiveness in improving MLP training for ML-based nonadiabatic dynamics. Our results show that the Δ²-based ML-ESMD method can reproduce ab initio ESMD simulations, underscoring its potential and efficiency for broader applications, particularly in large-scale and long-time scale ESMD simulations.

Magnesium, the lightest engineering metal, has MgO and Mg(OH)₂ as its common corrosion products, which can also be used for CO₂ storage due to their chemical reactivity. In this study, we developed a DFTB model with monopole, dipole, and quadrupole electrostatics for magnesium compounds containing oxygen, hydrogen, and carbon and applied it in both static and molecular dynamics (DFTB-MD) calculations of the MgO and Mg(OH)₂ hydration and carbonation processes. With our new model, the Electron Localization Function (ELF) and Charge Density Difference (CDD) were computed as part of the electronic structure analysis, providing insights into the electronic mechanism of MgO and Mg(OH)₂ hydration and carbonation processes. The geometry for the brucite-water bulk system was analyzed, including the reconstruction of near-surface water molecules which may influence the dissolution, hydration, and carbonation processes. By comparing experimental, DFT, classical MD results and the results from other parameter set, the accuracy of the model was assessed. A strong covalent bond between CO₂ and the (001) surface of MgO leads to the formation of a CO₃ group, while no such CO₃ group forms on the (101̅1) surface of Mg(OH)₂. Defect sites, however, are more favorable for the formation of the CO₃ group. In contrast, covalent bonds are not found for either surface when water interacted with them. This work provides new insights into the behavior of magnesium compounds interacting with water and carbon dioxide using our model, and it introduces a tool for effectively analyzing chemical electronic structures and bonding mechanisms.