Journal of Chemical Theory and Computation最新文献

An understanding of motor pairing dynamics as governed by two-body interactions underpins the elucidation of their self-assembly and collective behavior. In this work, we employ a hybrid molecular dynamics-multiparticle collision dynamics (MD-MPC) approach to develop a particle-based model of a Janus-sphere dimer motor together with its surrounding complex, multicomponent active fluid environment. The conformational dynamics of the motor pair, in the presence of concentration coupling arising from various catalytic distributions, is microscopically investigated. Multiple conformational states were observed, including 8 stable and 13 long-lived metastable configurations. These motor pairs exhibited a variety of modes of motion, including cyclic, rotational, helical, and oscillatory dynamics. By analyzing diffusiophoretic forces arising from chemical concentration gradients, we elucidate the physical mechanisms underlying these diverse configurations. The systematic characterization of motor-pair dynamics and conformational states provides a mechanistic foundation, thereby enabling the extension of this framework to studies of higher-order self-assembly and collective phenomena in chemically active, many-body systems.

Proteins are crucial in biological processes and are important substances that mediate biochemical reactions, regulate cellular processes, and facilitate drug binding through their active sites and surface residues. The pK_a values of proteins determine the protonation state of ionizable amino acids under specific pH conditions, profoundly impacting protein structure, function, and drug design. However, experimental determination of pK_a values is normally laborious and complex. Moreover, existing prediction methods are limited by the data quantity and quality, as well as their inability to address the intricate structural and physicochemical attributes of proteins, thereby hindering accuracy and generalization, especially in predicting pK_a values for buried residues. In this study, we developed a multimodal protein pK_a prediction model named ME-pK_a (Multimodal ESM pK_a), which leverages the multimodal information and employs a multifidelity learning strategy to speedily and accurately predict molecular pK_a values. The ME-pK_a method facilitates data augmentation by integrating the local environmental attributes of amino acids with the FASTA sequence characteristics of proteins. Furthermore, the incorporation of multifidelity learning has addressed the challenge of limited data availability to some extent. Our ME-pK_a model outperforms several state-of-the-art models in predicting protein pK_a values, achieving impressive results with a low RMSE of 0.845 ± 0.09 and MAE of 0.641 ± 0.07, a high R² of 0.921 ± 0.02, and R of 0.959 ± 0.01 on the PE-pK_a data set. Notably, ME-pK_a demonstrated balanced and robust performance across major ionizable residue types (ASP, GLU, HIS, LYS). It demonstrates superior accuracy in predicting pK_a values for buried residues (RSA < 0.2), achieving the lowest MAE values of 0.921 ± 0.05 on the PE-pK_a data set and 0.911 ± 0.06 on the Small Set data set, which collectively excel in capturing complex environmental influences on pK_a. Moreover, our method confirmed pH-dependent binding of PD-L1 antibodies mediated by the protonation state of His-69 in PD-L1, emphasizing the critical role of amino acid protonation states in drug design. The source code of ME-pK_a can be found at https://github.com/yzjyg215/ME-pKa.

Accurately and efficiently predicting the equilibrium geometries of large molecules remains a central challenge in quantum computational chemistry, even with hybrid quantum–classical algorithms. Two major obstacles hinder progress: the large number of qubits required and the prohibitive cost of conventional nested optimization. In this work, we introduce a co-optimization framework that combines Density Matrix Embedding Theory (DMET) with Variational Quantum Eigensolver (VQE) to address these limitations. This approach substantially reduces the required quantum resources, enabling the treatment of molecular systems significantly larger than previously feasible. We first validate our framework on benchmark systems, such as H₄ and H₂O₂, before demonstrating its efficacy in determining the equilibrium geometry of glycolic acid (C₂H₄O₃)─a molecule of a size previously considered intractable for quantum geometry optimization. Our results show the method achieves high accuracy while drastically lowering computational cost. This work thus represents a significant step toward practical, scalable quantum simulations, moving beyond the small, proof-of-concept molecules that have historically dominated the field. More broadly, our framework establishes a tangible path toward leveraging quantum advantage for the in silico design of complex catalysts and pharmaceuticals.

Understanding how molecular events in ion channels impact neuronal excitability, as derived from the calculation of the time course of the membrane potentials, can help elucidate the mechanisms of neurological disease-linked mutations and support neuroactive drug design. Here, we propose a multiscale simulation approach which couples molecular simulations with neuronal simulations to predict the variations in membrane potential and neural spikes. We illustrate this through two examples. First, molecular dynamics simulations predict changes in current and conductance through the AMPAR neuroreceptor when comparing the wild-type protein with certain disease-associated variants. The results of these simulations inform morphologically detailed models of cortical pyramidal neurons, which are simulated using the Arbor framework to determine neural spike activity. Based on these multiscale simulations, we suggest that disease associated AMPAR variants may significantly impact neuronal excitability. In the second example, the Arbor model is coupled with coarse-grained Monte Carlo gating simulations of voltage-gated (K⁺ and Na⁺) channels. The predicted current from these ion channels altered the membrane potential and, in turn, the excitation state of the neuron was updated in Arbor. The resulting membrane potential was then fed back into the Monte Carlo simulations of the voltage-gated ion channels, resulting in a bidirectional coupling of current and membrane potential. This allowed the transitions of the states of the ion channels to influence the membrane potentials and vice versa. Our Monte Carlo simulations also included the crucial, so far unexplored, effects of the composition of the lipid membrane embedding. We explored the influence of lipidic compositions only using the Monte Carlo simulations. Our combined approaches, which use several simplifying assumptions, predicted membrane potentials consistent with electrophysiological recordings and established a multiscale framework linking the atomistic perturbations to neuronal excitability.

In several biological processes, such as looping, supercoiling, and DNA–protein interactions, DNA is subject to very strong deformations. While coarse-grained models often approximate DNA as a smoothly bendable polymer, experimental and theoretical studies have demonstrated that mechanical stress can induce localized kinks. Here, we employ the Rigid Base Biasing of Nucleic Acids (RBB-NA) algorithm to systematically probe the properties of highly deformed DNA in all-atom simulations of short dodecamers. A simultaneous bias in bending (roll) and twist is applied locally to two consecutive base pairs in the center of the dodecamers. Using umbrella sampling, we construct free energy landscapes that reveal sequence-dependent effects for kink formation and quantify the energetic cost of kinking. We identify distinct features in the free energy profiles highlighting anharmonic effects, such as asymmetries in the positive vs negative roll. Our analysis suggests two distinct kink types characterized either by positive roll and undertwisting (twist-bend kinks) or by negative roll without excess twist (pure bend kinks). The former are frequently observed in DNA–protein structures and are expected to be favored in vivo in negatively supercoiled chromosomes. The latter has been observed in DNA simulations of minicircles and is favored in torsionally constrained DNA.

Quantification of the electron-vibrational couplings in lanthanide complexes and identification of the vibrations that are strongly coupled to electronic transitions are important for applications of optical lanthanide spectroscopy. While information about the electron-vibrational couplings can be extracted from the emission spectra, this is not common because of the challenges associated with interpreting the complex vibronic structures of the spectra. To overcome this challenge, we develop a fully ab initio methodology for predicting the vibronic peaks in the emission spectra of lanthanide complexes by calculating the electron-vibrational couplings associated with individual vibrational modes. We show that the energy gradients of the emitting and ground spin–orbit states, which are the key quantities required for calculating the electron-vibrational couplings, can be obtained analytically from the energy gradients of the spin-diabatic states and the corresponding nonadiabatic coupling matrix elements. To illustrate this methodology, we calculate the ⁴F_9/2→⁴I_15/2 emission spectrum of the erbium trensal complex, provide the full decomposition of the vibronic structure of the spectrum, and investigate the effects of spin–orbit interaction and metal–ligand hybridization on the electron-vibrational couplings. In addition, to validate our methodology, we calculate and compare with experiment the vibronic structure of the ⁴S_3/2→⁴I_15/2 green emission band.

We present a reusable, open-source software implementation of the second-order trust region algorithm in the new OpenTrustRegion library. We apply the implementation to the general-purpose optimization of molecular orbitals in various contexts within electronic-structure theory. Our permissibly licensed implementation can be included in any software package, be it free and open-source, academically licensed closed-source, or commercial. Detailing the implementation in OpenTrustRegion, we present a review of the theory behind trust region-based methods alongside various extensions. We demonstrate the robustness and efficiency of our optimization library with extensive benchmarks for self-consistent field calculations, orbital localization, as well as orbital symmetrization tasks, featuring challenging and pathological systems.

The solid electrolyte interphase (SEI) plays a crucial regulatory role in the electrochemical reversibility of lithium-ion batteries, yet understanding of its formation mechanism remains limited due to compositional complexity. By integrating a multiscale simulation framework combining density functional theory (DFT), molecular dynamics (MD) and the REACTER protocol, which dynamically updates molecular topologies to simulate bond-breaking and formation in fixed-valence force fields, enhanced with topology-mapped reaction templates and physics-informed constraints, we elucidate the atomistic mechanisms governing the initial formation of the SEI on pristine and functionalized graphite anodes (O-terminated, OH-terminated, and O/OH-terminated). Simulation results reveal that functionalized graphite surfaces universally exhibit three-stage SEI growth kinetics: rapid initial formation, transition regulation, and steady-state growth phases. A key finding reveals that OH-terminated surfaces accelerate the formation of thin but densely structured inorganic/organic composite SEI layers, which effectively suppress component dissolution into the electrolyte. This optimized interface exhibits superior transport properties within the interfacial region between the SEI and the electrolyte, demonstrating enhanced ionic conductivity and favorable viscosity characteristics. Our multiscale analysis highlights electrode surface functionalization as a highly promising strategy for controlling SEI growth mechanisms, providing fundamental principles for the rational design of high-performance battery interfaces.

Characterizing an electronic excitation in terms of its underlying orbital reorganization is central to understanding photochemical and photophysical processes. Here, we introduce an excitation/relaxation framework that separates electron promotion from orbital relaxation contributions within Δ-self-consistent-field (ΔSCF) treatments of electronic transitions. The framework defines the excitation number and the relaxation number, which quantify electron promotion and electron relaxation. Formulated in terms of difference density natural orbitals (DDNOs), the approach generalizes earlier attachment/detachment and natural ionization orbital models. A compact set of modified Slater–Condon rules derived in the DDNO basis enables direct evaluation of transition dipole moments and oscillator strengths. Application to a test set of 5 molecules and 19 ΔSCF excitations demonstrates that the model yields integer excitation numbers and interpretable relaxation numbers, and that the corresponding DDNOs can be visualized to display particle/hole and relaxation pairs.

To enable the accurate prediction of solvent effects on nonadiabatic processes, time-dependent density functional theory (TDDFT) is combined with the effective fragment potential (EFP) method for the computation of nonadiabatic coupling matrix elements (NACME). The viability of the new TDDFT/EFP NACME method is demonstrated via comparisons to fully TDDFT NACME calculations for both the solute (methylene imine) and solvent (water or methanol). The success of the combined TDDFT/EFP method is promising for the use of the new method for accurate simulations of the nonadiabatic dynamics of solvated molecules.