Journal of Chemical Theory and Computation最新文献

In this work, we explore the use of the one-particle reduced density matrix (1RDM) to streamline energy measurements of chemical systems on quantum computers, particularly within the variational quantum eigensolver (VQE) framework. This approach leverages the existence of an exact energy functional of the 1RDM, enabling a reduction in both the number of expectation values to measure and the number of circuits to execute, thereby optimizing quantum resource usage. Specifically, sampling the 1RDM involves measuring only [Formula: see text] elements, which is significantly fewer than the [Formula: see text] required for the Hamiltonian's expectation value ⟨Ĥ⟩. We demonstrate our approach by harnessing the well-established natural orbital functional (NOF) theory, using the natural orbitals and occupation numbers derived from the diagonalization of the 1RDM measured from the quantum computer. Starting with the H₂ system, we validate the accuracy of our method by comparing the energy derived from NOF approximations applied to the exact wave function with the value obtained from ⟨Ĥ⟩. This is followed by an optimization of the gate parameters by minimizing the energy using the NOF approximations as the objective function. The analysis is extended to larger systems, such as LiH, Li₂, OH^-, FH, NeH⁺, and F₂ using a wave function ansatz with single and double excitation gates. This NOF-based method reduces the scaling cost of circuit executions compared to standard VQE implementations, achieving around 90% savings in the systems used in this work. Overall, by using a well-performing NOF as the objective function, the proposed NOF-VQE demonstrates the viability of NOF approximations for obtaining accurate energies in the noisy intermediate-scale quantum era and underscores the potential for developing new functionals tailored to quantum computing applications.

Intrinsically disordered proteins (IDPs), characterized by a lack of defined tertiary structure, are ubiquitous and indispensable components of cellular machinery. These proteins participate in a diverse array of biological processes, often undergoing conformational transitions upon binding to their target, a phenomenon termed "folding-upon-binding." The finding raises the question of how to achieve rapid binding kinetics in the presence of intrinsic disorder, and the underlying molecular mechanism remains elusive. This study investigated the interaction between the C-terminal region of CRIPT and the third PDZ domain of PSD-95, a critical complex in neuronal development. Upon binding, the CRIPT peptide adopts a β-strand conformation, a process meticulously characterized through extensive molecular dynamics simulations totaling 67.7 μs. Our findings reveal a funnel-like binding landscape in which IDPs can adopt multiple conformations prior to binding, forming structurally heterogeneous intermediate complexes and leading to diverse binding pathways. The stabilization of these intermediate complexes necessitates a dynamic interplay of native and non-native interactions. Markov state model analysis underscores the important role of structural heterogeneity as it contributes to accelerated binding. These findings enrich the classical fly-casting mechanism and provide novel insights into the functional advantages conferred by intrinsic disorder.

The classical Lewis-Langmuir electron pair model remains central to chemical bonding theories despite its inherent contradictions with quantum mechanical principles such as antisymmetry. This paper revisits the long-forgotten Linnett's double quartet (LDQ) model, which integrates spin considerations into chemical bonding. We demonstrate that the distribution of electrons at the maxima of the square of the wave function (Born maxima) highlights the rigidity of the same-spin electron blocks and validates the LDQ framework in atoms and molecules. A generalized LDQ model accounts for all bond types, including covalent, polar covalent, ionic, dative, and electron-deficient, and directly incorporates electron correlation effects, providing a rigorous yet intuitive approach to bonding. This perspective also reveals fundamental flaws in conventional mean-field descriptions that ignore the correlated motion of electrons. By bridging traditional and quantum paradigms, the generalized LDQ model offers a robust tool for understanding chemical bonding, with implications for education, experimental design, and theoretical advancements.

We present an efficient, asymptotically linear-scaling implementation of the canonically $O\left(N^8\right)$ coupled-cluster method with singles, doubles, and full triples excitations (CCSDT) method. We apply the domain-based local pair natural orbital (DLPNO) approach for computing CCSDT amplitudes. Our method, called DLPNO-CCSDT, uses the converged coupled-cluster amplitudes from a preceding DLPNO-CCSD(T) computation as a starting point for the solution of the CCSDT equations in the local natural orbital basis. To simplify the working equations, we t₁-dress our two-electron integrals and Fock matrices, allowing our equations to take on the form of CCDT. With appropriate parameters, our method can recover more than 99.99% of the total canonical CCSDT correlation energy. In addition, we demonstrate that our method consistently yields sub-kJ mol^-1 errors in relative energies when compared to canonical CCSDT, and, likewise, when computing the difference between CCSDT and CCSD(T). Finally, to highlight the low scaling of our algorithm, we present timings on linear alkanes (up to 30 carbons and 730 basis functions) and water clusters (up to 131 water molecules and 3144 basis functions).

Finite-temperature lattice free energy differences between polymorphs of molecular crystals are fundamental to understanding and predicting the relative stability relationships underpinning polymorphism, yet are computationally expensive to obtain. Here, we implement and critically assess machine-learning-enabled targeted free energy calculations derived from flow-based generative models to compute the free energy difference between two ice crystal polymorphs (Ice XI and Ic), modeled with a fully flexible empirical classical force field. We demonstrate that even when remapping from an analytical reference distribution, such methods enable a cost-effective and accurate calculation of free energy differences between disconnected metastable ensembles when trained on locally ergodic data sampled exclusively from the ensembles of interest. Unlike classical free energy perturbation methods, such as the Einstein crystal method, the targeted approach analyzed in this work requires no additional sampling of intermediate perturbed Hamiltonians, offering significant computational savings. To systematically assess the accuracy of the method, we monitored the convergence of free energy estimates during training by implementing an overfitting-aware weighted averaging strategy. By comparing our results with ground-truth free energy differences computed with the Einstein crystal method, we assess the accuracy and efficiency of two different model architectures, employing two different representations of the supercell degrees of freedom (Cartesian vs quaternion-based). We conduct our assessment by comparing free energy differences between crystal supercells of different sizes and temperatures and assessing the accuracy in extrapolating lattice free energies to the thermodynamic limit. While at low temperatures and in small system sizes, the models perform with similar accuracy. We note that for larger systems and high temperatures, the choice of representation is key to obtaining generalizable results of quality comparable to that obtained from the Einstein crystal method. We believe this work to be a stepping stone toward efficient free energy calculations in larger, more complex molecular crystals.

Glasses are known to have medium-range order (MRO), but their link to any experimentally measurable quantity is still ambiguous. The first sharp diffraction peak (FSDP) in structure factor S(q) obtained from diffraction experiments on glasses has been associated with this MRO (∼7-15 Å), but understanding the fundamental origin of this universal peak is still an open problem. We have addressed this issue for a complex glass, i.e., iron phosphate glass (IPG), through atomistic models generated from a hybrid approach (our in-house-developed MC code + molecular dynamics simulation). IPG is a technologically important glass with applications in waste vitrification, bioactive glass, laser glass material, anode material for batteries, etc., and is seen as a strengthened substitute for borosilicate glasses. We performed a comparative study by generating glass models from different initial configurations and randomization techniques. The developed IPG models were first validated with existing data on short-range order (SRO) and MRO through the study of pair correlation functions, bond angle distributions, and coordination number for SRO and rings distribution, FSDP in structure factor, and void size distribution for MRO. The study of coordination environment of oxygen is specifically shown to aid in understanding glass formation through topological constraint theory. Thereafter, to understand the fundamental origin of FSDP in S(q), structure factors were calculated corresponding to the individual ring sizes present in the model. The relative contribution of these individual S(q)'s in the total experimental S(q) is estimated using an inverse fitting approach. The contributions thus obtained directly correlated with ring size percentages in the models for the considered q-range. In particular, the melt-quenched model obtained from the MC model as an initial structure is found to reproduce most experimental features seen in IPG. Through this exercise, we can connect the rings distribution of an atomistic glass model with an experimentally measurable quantity like FSDP in S(q) for a complex glass-like IPG. This gives physical meaning to the rings distribution while also proving that this structural descriptor is a useful tool for validation of MRO in simulation-produced models of glass.

Using trial wave functions prepared on quantum devices to reduce the bias of auxiliary-field quantum Monte Carlo (QC-AFQMC) has established itself as a promising hybrid approach to the simulation of strongly correlated many body systems. Here, we further reduce the required quantum resources by decomposing the trial wave function into classical and quantum parts, respectively treated by classical and quantum devices, within the contextual subspace projection formalism. Importantly, we show that our algorithm is compatible with the recently developed matchgate shadow protocol for efficient overlap calculation in QC-AFQMC. Investigating the nitrogen dimer and the reductive decomposition of ethylene carbonate in lithium-based batteries, we observe that our method outperforms a number of established algorithm for ground state energy computations, while reaching chemical precision with less than half of the original number of qubits.

We present algebraic diagrammatic construction theory for simulating spin-orbit coupling and electron correlation in charged electronic states and photoelectron spectra. Our implementation supports Hartree-Fock and multiconfigurational reference wave functions, enabling efficient correlated calculations of relativistic effects using single-reference (SR-) and multireference-algebraic diagrammatic construction (MR-ADC). We combine the SR- and MR-ADC methods with three flavors of spin-orbit two-component Hamiltonians and benchmark their performance for a variety of atoms and small molecules. When multireference effects are not important, the SR-ADC approximations are competitive in accuracy to MR-ADC, often showing closer agreement with experimental results. However, for electronic states with multiconfigurational character and in nonequilibrium regions of potential energy surfaces, the MR-ADC methods are more reliable, predicting accurate excitation energies and zero-field splittings. Our results demonstrate that the spin-orbit ADC methods are promising approaches for interpreting and predicting the results of modern spectroscopies.

Molecular dynamics (MD) offers important insights into intrinsically disordered peptides and proteins (IDPs) at a level of detail that often surpasses that available through experiments. Recent studies indicate that MD force fields do not reproduce intrinsic conformational ensembles of amino acid residues in water well, which limits their applicability to IDPs. We report a new MD force field, Amber ff24EXP-GA, derived from Amber ff14SB by optimizing the backbone dihedral potentials for guest glycine and alanine residues in cationic GGG and GAG peptides, respectively, to best match the guest residue-specific spectroscopic data. Amber ff24EXP-GA outperforms Amber ff14SB with respect to conformational ensembles of all 14 guest residues x (G, A, L, V, I, F, Y, D^p, E^p, R, C, N, S, T) in GxG peptides in water, for which complete sets of spectroscopic data are available. Amber ff24EXP-GA captures the spectroscopic data for at least 7 guest residues (G, A, V, F, C, T, E^p) better than CHARMM36m and exhibits more amino acid specificity than both the parent Amber ff14SB and CHARMM36m. Amber ff24EXP-GA reproduces the experimental data on three folded proteins and three longer IDPs well, while outperforming Amber ff14SB on short unfolded peptides.

pH is an important physicochemical property that modulates proteins' structure and interaction patterns. A simple change in a site's protonation state in an enzyme's catalytic pocket can strongly alter its activity and its affinity to substrate, products, or inhibitors. We addressed this pH effect issue by evaluating its impact on donepezil binding to acetylcholinesterase (AChE). We compared the binding affinities obtained from molecular docking (weighted from the protonation states sampled by constant-pH MD) with those from molecular mechanics/Poisson-Boltzmann surface area and isothermal titration calorimetry data. The computational methods showed a clear trend where donepezil binding to the catalytic cavity is improved with the drug protonation (lowering pH). However, the loss of binding affinity observed experimentally at pH 6.0 indicates that other phenomena eluding our computational approaches are occurring. Possible factors include the shape of the access tunnel to the AChE catalytic pocket (which is captured in our MD time scale) or an entropic penalty difference between neutral and protonated donepezil. Altogether, this work highlighted the need to improve our computational methods to capture the pH effects in protein/drug binding, while also exposing the limitations that will inevitably arise from these new advances.