Journal of Chemical Theory and Computation最新文献

The nuclear-electronic orbital (NEO) approach incorporates nuclear quantum effects into quantum chemistry calculations by treating specified nuclei quantum mechanically, equivalently to the electrons. Within the NEO framework, excited states are vibronic states representing electronic and nuclear vibrational excitations. The NEO multireference configuration interaction (MRCI) method presented herein provides accurate ground and excited vibronic states. The electronic and nuclear orbitals are optimized with a NEO multiconfigurational self-consistent field (NEO-MCSCF) procedure, thereby including both static and dynamic correlation and allowing the description of double and higher excitations. The accuracy of the NEO-MRCI method is illustrated by computing the ground state protonic densities and excitation energies of the vibronic states for four molecular systems with the hydrogen nucleus treated quantum mechanically. In addition, revised conventional electronic basis sets adapted for quantized nuclei are developed and shown to be essential for achieving this level of accuracy. The NEO-MRCI approach, as well as the strategy for revising electronic basis sets, will play a critical role in multicomponent quantum chemistry.

Molecular Dynamics (MD) simulations are a versatile tool to investigate the interactions of proteins within their environments, in particular, of membrane proteins with the surrounding lipids. However, quantitative analysis of lipid-protein binding kinetics has remained challenging due to considerable noise and low frequency of long binding events, even in hundreds of microseconds of simulation data. Here, we apply Bayesian nonparametrics to compute residue-resolved residence time distributions from MD trajectories. Such an analysis characterizes binding processes at different time scales (quantified by their kinetic off-rate) and assigns to each trajectory frame a probability of belonging to a specific process. In this way, we classify trajectory frames in an unsupervised manner and obtain, for example, different binding poses or molecular densities based on the time scale of the process. We demonstrate our approach by characterizing interactions of cholesterol with six different G-protein-coupled receptors (A_2AAR, β₂AR, CB₁R, CB₂R, CCK₁R, and CCK₂R) simulated with coarse-grained MD simulations with the MARTINI model. The nonparametric Bayesian analysis allows us to connect the coarse binding time series data to the underlying molecular picture and thus not only infers accurate binding kinetics with error distributions from MD simulations but also describes molecular events responsible for the broad range of kinetic rates.

The accurate computational study of wavepacket nuclear dynamics is considered to be a classically intractable problem, particularly with increasing dimensions. Here, we present two algorithms that, in conjunction with other methods developed by us, may result in one set of contributions for performing quantum nuclear dynamics in arbitrary dimensions. For one of the two algorithms discussed here, we present a direct map between the Born-Oppenheimer Hamiltonian describing the nuclear wavepacket time evolution and the control parameters of a spin-lattice Hamiltonian that describes the dynamics of qubit states in an ion-trap quantum computer. This map is exact for three qubits, and when implemented, the dynamics of the spin states emulates those of the nuclear wavepacket in a continuous representation. However, this map becomes approximate as the number of qubits grows. In a second algorithm, we present a general quantum circuit decomposition formalism for such problems using a method called the Quantum Shannon Decomposition. This algorithm is more robust and is exact for any number of qubits at the cost of increased circuit complexity. The resultant circuit is implemented on IBM's quantum simulator (QASM) for 3-7 qubits, without using a noise model so as to test the intrinsic accuracy of the method. In both cases, the wavepacket dynamics is found to be in good agreement with the classical propagation result and the corresponding vibrational frequencies obtained from the wavepacket density time evolution are in agreement to within a few tenths of a wavenumber.

Multicomponent polymer mixtures are ubiquitous in biological self-organization but are notoriously difficult to study computationally. Plagued by both slow single molecule relaxation times and slow equilibration within dense mixtures, molecular dynamics simulations are typically infeasible at the spatial scales required to study the stability of mesophase structure. Polymer field theories offer an attractive alternative, but analytical calculations are only tractable for mean-field theories and nearby perturbations, constraints that become especially problematic for fluctuation-induced effects such as coacervation. Here, we show that a recently developed technique for obtaining numerical solutions to partial differential equations based on operator learning, neural operators, lends itself to a highly scalable training strategy by parallelizing per-species operator maps. We illustrate the efficacy of our approach on six-component mixtures with randomly selected compositions and that it significantly outperforms the state-of-the-art pseudospectral integrators for field-theoretic simulations, especially as polymer lengths become long.

Accurate yet efficient modeling of chemical systems with pronounced static correlation in their excited states remains a significant challenge in quantum chemistry, as most electronic structure methods that can adequately capture static correlation scale factorially with system size. Researchers are often left with no option but to use more affordable methods that may lack the accuracy required to model critical processes in photochemistry such as photolysis, photocatalysis, and nonadiabatic relaxation. A great deal of work has been dedicated to refining single-reference descriptions of static correlation in the ground state via "addition-by-subtraction" coupled cluster methods such as pair coupled cluster with double substitutions (pCCD), singlet-paired CCD (CCD0), triplet-paired CCD (CCD1), and CCD with frozen singlet- or triplet-paired amplitudes (CCDf0/CCDf1). By combining wave functions derived from these methods with the intermediate state representation (ISR), we gain insights into the extensibility of single-reference coupled cluster theory's coverage of static correlation to the excited state problem. Our CCDf1-ISR(2) approach is robust in the face of static correlation and provides enough dynamical correlation to accurately predict excitation energies to within about 0.2 eV in small organic molecules. We also highlight distinct advantages of the Hermitian ISR construction, such as the avoidance of pathological failures of equation-of-motion methods for excited state potential energy surface topology. Our results prompt us to continue exploring optimal single-reference theories (excited state approaches that leverage dependence on the initial reference wave function) as a potentially economical approach to the excited state static correlation problem.

Swift discovery of spin-crossover materials for their potential application in electronic and quantum devices requires techniques that enable efficient identification of suitable candidates. To this end, we screened the Cambridge Structural Database to develop a specialized database of 1439 materials and computed spin-switching energies from density functional theory for each material. The database was used to train an equivariant graph convolution neural network to predict the magnitude of the spin-conversion energy. A test mean absolute error was 360 meV. For candidate identification, we equipped the system with a relevance-based classifier. This approach leads to a nearly 4-fold improvement in identifying potential spin-crossover systems of interest as compared to conventional high-throughput screening.

Perturbative approaches are methods to efficiently tackle many-body problems, offering both intuitive insights and analysis of correlation effects. However, their application to systems where light and matter are strongly coupled is nontrivial. Specifically, the definition of suitable orbitals for the zeroth-order Hamiltonian represents a significant theoretical challenge. While reviewing previously investigated orbital choices, this work presents an alternative polaritonic orbital basis suitable for the strong coupling regime. We develop a quantum electrodynamical (QED) Møller-Plesset perturbation theory using orbitals obtained from the strong coupling QED Hartree-Fock. We assess the strengths and limitations of the different approaches with emphasis on frequency and coupling strength dispersions, intermolecular interactions and polarization orientational effects. The results show the essential role of using a consistent molecular orbital framework in order to achieve an accurate description of cavity-induced electron-photon correlation effects.

Quantum computing (QC) provides a promising avenue for enabling quantum chemistry calculations, which are classically impossible due to computational complexity that increases exponentially with system size. As fully fault-tolerant algorithms and hardware, for which an exponential speedup is predicted, are currently out of reach, recent research efforts have been dedicated to developing and scaling algorithms for Noisy Intermediate-Scale Quantum (NISQ) devices to showcase the practical usefulness of such machines. To demonstrate the usefulness of NISQ devices in the field of chemistry, we apply our recently developed FAST-VQE algorithm and a state-of-the-art quantum gate reduction strategy based on propositional satisfiability together with standard optimization tools for the simulation of the rate-determining proton transfer step for CO₂ hydration catalyzed by carbonic anhydrase resulting in the first application of a quantum computing device for the simulation of an enzymatic reaction. To this end, we have combined classical force field simulations with quantum mechanical methods on classical and quantum computers in a hybrid calculation approach. The presented technique significantly enhances the accuracy and capabilities of QC-based molecular modeling and finally pushes it into compelling and realistic applications. The framework is general and can be applied beyond the case of computational enzymology.

We present an algorithmic approach to optimize chain propagator computations in polymer field theory simulations, including self-consistent field theory (SCFT) calculations and field-theoretic simulations (FTSs). Propagator calculations for branched block copolymers often involve recursive structures and overlapping subproblems, resulting in redundant computations. By employing dynamic programming (DP) and encoding computational dependencies as strings, our method systematically eliminates these redundancies in mixtures of branched polymers. The algorithm achieves optimal time complexity for various polymeric systems, including star-shaped, comb, dendrimer polymers, and homopolymer mixtures, by reusing and aggregating propagators for symmetric and repetitive structures. This enhances computational efficiency and reduces memory usage, addressing a key limitation in developing versatile polymer field theory simulation software. Our approach streamlines the simulation of complex branched polymers without requiring manual software adjustments, facilitating more efficient workflows for polymer researchers. Furthermore, the method enables automated searches for inverse design by optimizing computations across diverse branched polymer architectures, contributing to the discovery and design of novel polymeric materials. The algorithm is implemented in open-source software, ensuring accessibility for further development and broader application in computational polymer science.

The definition of heat current operator for systems for nonpairwise additive interactions and its impact on related lattice thermal conductivity (κ_L) via molecular dynamics (MD) simulation are ambiguous and controversial when migrating from empirical potential models to machine learning potential (MLP) models. Herein, we study and compare the significance of many-body interaction with heat current computation in one of the most popular MLP models, the moment tensor potential (MTP). Nonequilibrium MD simulations and equilibrium MD simulations among four different materials were performed, and inconsistencies in energy conservation between the simulation thermostat and the pairwise calculator were found. A new virial stress tensor expression with a many-body heat current description was integrated inside the MTP, and we uncovered the influence of the modification that could alter the κ_L results by 29-64% using the equilibrium MD computational approach. Our work demonstrates the importance of a many-body description during thermal analysis in MD simulations when MLPs are of concern.