Dynamics Of Molecular Systems Research Articles

Probing the sensitivity of ab initio multiple spawning to its parameters

Full multiple spawning (FMS) offers a strategy to simulate the nonadiabatic dynamics of molecular systems by describing their nuclear wavefunctions by a linear combination of coupled trajectory basis functions (TBFs). Applying a series of controlled approximations to the full multiple spawning (FMS) equations leads to the ab initio multiple spawning (AIMS), which is compatible with an on-the-fly propagation of the TBFs and an accurate description of nonadiabatic processes. The AIMS strategy and its numerical implementations, however, rely on a series of user-defined parameters. Herein, we investigate the influence of these parameters on the electronic-state population of two molecular systems— trans-azomethane and a two-dimensional model of the butatriene cation. This work highlights the stability of AIMS with respect to most of its parameters, underlines the specific parameters that require particular attention from the user of the method, and offers prescriptions for an informed selection of their value.

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

It is known that the behavior of many complex systems is controlled by local dynamic rearrangements or fluctuations occurring within them. Complex molecular systems, composed of many molecules interacting with each other in a Brownian storm, make no exception. Despite the rise of machine learning and of sophisticated structural descriptors, detecting local fluctuations and collective transitions in complex dynamic ensembles remains often difficult. Here, we show a machine learning framework based on a descriptor which we name Local Environments and Neighbors Shuffling (LENS), that allows identifying dynamic domains and detecting local fluctuations in a variety of systems in an abstract and efficient way. By tracking how much the microscopic surrounding of each molecular unit changes over time in terms of neighbor individuals, LENS allows characterizing the global (macroscopic) dynamics of molecular systems in phase transition, phases-coexistence, as well as intrinsically characterized by local fluctuations (e.g., defects). Statistical analysis of the LENS time series data extracted from molecular dynamics trajectories of, for example, liquid-like, solid-like, or dynamically diverse complex molecular systems allows tracking in an efficient way the presence of different dynamic domains and of local fluctuations emerging within them. The approach is found robust, versatile, and applicable independently of the features of the system and simply provided that a trajectory containing information on the relative motion of the interacting units is available. We envisage that "such a LENS" will constitute a precious basis for exploring the dynamic complexity of a variety of systems and, given its abstract definition, not necessarily of molecular ones.

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals ** ** Notice: This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license

Understanding the mechanics and failure of materials at the nanoscale is critical for their engineering and applications. The accurate atomistic modeling of brittle failure with crack propagation in covalent crystals requires a quantum mechanics-based description of individual bond-breaking events. Artificial neural network potentials (NNPs) have emerged to overcome the traditional, physics-based modeling tradeoff between accuracy and accessible time and length scales. Previous studies have shown successful applications of NNPs for describing the structure and dynamics of molecular systems and amorphous or liquid phases of materials. However, their application to deformation and failure processes in materials is still uncommon. In this study, we discuss the apparent limitations of NNPs for the description of deformation and fracture under loadings and propose a way to generate and select training data for their employment in simulations of deformation and fracture simulations of crystals. We applied the proposed approach to 2D crystalline graphene, utilizing the density-functional tight-binding method for more efficient and extensive data generation in place of density functional theory. Then, we explored how the data selection affects the accuracy of the developed artificial NNPs. It revealed that NNP’s reliability should not only be measured based on the total energy and atomic force comparisons for reference structures but also utilize comparisons for physical properties, e.g. stress–strain curves and geometric deformation. In sharp contrast to popular reactive bond order potentials, our optimized NNP predicts straight crack propagation in graphene along both armchair and zigzag (ZZ) lattice directions, as well as higher fracture toughness of ZZ edge direction. Our study provides significant insight into crack propagation mechanisms on atomic scales and highlights strategies for NNP developments of broader materials.

QM/AMOEBA description of properties and dynamics of embedded molecules

AbstractWe describe the development, implementation, and application of a polarizable QM/MM strategy, based on the AMOEBA polarizable force field, for calculating molecular properties and performing dynamics of molecular systems embedded in complex matrices. We show that polarizable QM/MM is a well‐understood, mature technology that can be deployed using a state‐of‐the‐art implementation that combines efficient numerical methods and linear scaling techniques. Thanks to these numerical advances and to the availability of parameters for a wide manifold of systems in the AMOEBA force field, polarizable QM/AMOEBA can be used for advanced production applications, that range from the prediction of spectroscopies to ground‐ and excited‐state multiscale ab initio molecular dynamics simulations.This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Electronic Structure Theory > Combined QM/MM Methods

Ultrafast multiexciton dynamics in molecular systems: Inclusion of exciton-exciton annihilation

We report simulations of exciton-exciton annihilation (EEA) in dye molecular aggregates with explicit laser pulse excitation in the framework of reduced density matrix theory. We use a supramolecular complex model Hamiltonian, in which each monomer of an aggregate is described by three electronic levels. An expectation value approximation is used to avoid the large amount of matrix elements for various exciton configuration distributions in molecular aggregates. Quantum fluctuations truncation is applied to derive the closed equations of motion for single- and two-site functions. Using these equations, we simulate the laser pulse driven EEA dynamics, and compare it with rate equations to highlight the importance of quantum coherence effects. We also discuss the effects of the aggregate configuration, the molecular transition dipoles and the duration of laser pulses on EEA. Furthermore, we analyze the relation between the coherence time of fusion and the annihilation process. Our study indicates that EEA process is more efficient for coherent excitation at the lowest energy level in J aggregates. Our simulation of the normalized population of the first excited state for various field amplitudes can qualitatively reproduce time-resolved experimental emission spectra. This confirms that the fundamental description of laser pulse induced EEA is correct within our model.

Overcoming positivity violations for density matrices in surface hopping.

Fewest-switches surface hopping (FSSH) has emerged as one of the leading methods for modeling the quantum dynamics of molecular systems. While its original formulation was limited to adiabatic populations, the growing interest in the application of FSSH to coherent phenomena prompts the question of how one should construct a complete density matrix based on FSSH trajectories. A straightforward solution is to define adiabatic coherences based on wavefunction coefficients. In this paper, we demonstrate that inconsistencies introduced in the density matrix through such treatment may lead to a violation of positivity. We furthermore show that a recently proposed coherent generalization of FSSH results in density matrices that satisfy positivity while yielding improved accuracy throughout much (but not all) of parameter space.

Data-driven construction of stochastic reduced dynamics encoded with non-Markovian features.

One important problem in constructing the reduced dynamics of molecular systems is the accurate modeling of the non-Markovian behavior arising from the dynamics of unresolved variables. The main complication emerges from the lack of scale separations, where the reduced dynamics generally exhibits pronounced memory and non-white noise terms. We propose a data-driven approach to learn the reduced model of multi-dimensional resolved variables that faithfully retains the non-Markovian dynamics. Different from the common approaches based on the direct construction of the memory function, the present approach seeks a set of non-Markovian features that encode the history of the resolved variables and establishes a joint learning of the extended Markovian dynamics in terms of both the resolved variables and these features. The training is based on matching the evolution of the correlation functions of the extended variables that can be directly obtained from the ones of the resolved variables. The constructed model essentially approximates the multi-dimensional generalized Langevin equation and ensures numerical stability without empirical treatment. We demonstrate the effectiveness of the method by constructing the reduced models of molecular systems in terms of both one-dimensional and four-dimensional resolved variables.

Generalized Quantum Master Equations for Simulating Electronic Dynamics in Molecular Systems

Generalized Quantum Master Equations for Simulating Electronic Dynamics in Molecular Systems

Intelligent dissipative particle dynamics: Bridging mesoscopic models from microscopic simulations via deep neural networks

We develop a bottom-up coarse graining framework to construct mesoscopic force fields directly from microscopic dynamics by a machine learning technique. This scheme, called the intelligent dissipative particle dynamics (IDPD), holds various consistencies, including configuration, phase, static and dynamic consistencies that are incorporated into a deep neural network. The network is trained with full atomic data in a special way for preserving the natural symmetries of the system, and generates both the conservative and non-conservative force fields in the mesoscopic system. Subsequently, the force fields are used in the dissipative particle dynamics (DPD) that is considered as the effective mesoscopic dynamics resulting from the Mori–Zwanzig projection of the underlying atomic dynamics. To show the validity and applicability of IDPD, we coarse grain star polymer and methane fluids respectively, from full molecular dynamics (MD) simulations. Quantitative comparisons between the MD and IDPD indicate that the IDPD is able to reproduce both the static and dynamic properties of underlying MD system, with the aids of a pressure correction and a diffusion rescaling, even when coarse graining multiple molecules into a particle. It follows that the IDPD is competent for preserving the dynamic properties and coarse graining non-bonded molecules, which are common challenges for most of coarse graining methods.

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network.

Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule- based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and dynamics, acquired through model simulations. In line with previous reports, we confirm that the Kappa model recapitulates the general dynamics of its ODE counterpart with minor differences. These occur when molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one. As reported for other molecular systems, we find that, also for the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, easing model reuse. In parallel with these analyses, we report a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of more complex dynamic models to study this important molecular system.

Staying the course: iteratively locating equilibria of dynamical systems on Riemannian manifolds defined by point-clouds

We introduce a method to successively locate equilibria (steady states) of dynamical systems on Riemannian manifolds. The manifolds need not be characterized by an a priori known atlas or by the zeros of a smooth map. Instead, they can be defined by point-clouds and sampled as needed through an iterative process. If the manifold is an Euclidean space, our method follows isoclines, curves along which the direction of the vector field $X$ is constant. For a generic vector field $X$, isoclines are smooth curves and every equilibrium lies on isoclines. We generalize the definition of isoclines to Riemannian manifolds through the use of parallel transport: generalized isoclines are curves along which the directions of $X$ are parallel transports of each other. As in the Euclidean case, generalized isoclines of generic vector fields $X$ are smooth curves that connect equilibria of $X$. Our algorithm can be regarded as an extension of the method of Newton trajectories to the manifold setting when the manifold is unknown. This work is motivated by computational statistical mechanics, specifically high dimensional (stochastic) differential equations that model the dynamics of molecular systems. Often, these dynamics concentrate near low-dimensional manifolds and have transitions (saddle points with a single unstable direction) between metastable equilibria. We employ iteratively sampled data and isoclines to locate these saddle points. Coupling a black-box sampling scheme (e.g., Markov chain Monte Carlo) with manifold learning techniques (diffusion maps in the case presented here), we show that our method reliably locates equilibria of $X$.

Thermal acclimation results in persistent phosphoproteome changes in the freshwater planarian Crenobia alpina (Tricladida: Planariidae)

Understanding the molecular mechanisms underlying thermal tolerance of aquatic invertebrates can inform predictions about the effects of thermal regime changes on these species. While gene expression and protein abundance changes underlie compensatory responses, little is known about the role of post-translational modifications as thermal tolerance mechanisms. To test the hypothesis that protein phosphorylation changes in response to thermal acclimation, we studied the phosphoproteome of the freshwater planarian Crenobia alpina. This species has a supposedly limited thermal tolerance and is found in cold-stable habitats. We systematically investigated phosphopeptide abundances following 168 h acclimation to 11, 14, 17, and 20 °C, using label-free quantitative phosphoproteomics. We provide a comparative analysis of 2115 phosphosites from 1049 phosphoproteins, whereby little to no differences could be observed between 11, 14, and 17 °C. However, more than 130 phosphopeptides were significantly more abundant and 40 were less abundant following acclimation to 20 °C. These phosphoproteins were functionally associated with the regulation of neuronal processes, cilia, DNA damage repair, aquaporins, and mitochondrial fission and fusion. These data support the hypothesis that phosphorylation plays a role in thermal acclimation responses, suggesting that PTMs are of significance in invertebrate thermal acclimation. PTMs may therefore offer an alternative route of transient protein adaptation to temperature increase in invertebrates, and should not be neglected when trying to understand how molecular system dynamics in response to elevated temperatures.

The role of the excluded volume in the molecular dynamics for molecular systems revealed by the direct computational approach

In this paper, we directly verify the methods of determining the free volumes proposed in the literature. Using the computer simulations of the molecular dynamics for two quasi-real model systems we estimate, in the possibly most direct way, the excluded volume, which is a quantity crucial for the calculation of the free volume. The values of the excluded volume obtained by us turn out to be much lower than those calculated by routinely used and recently proposed methods in the literature. Moreover, our results suggest that, in contrast to the typical theoretical models, the excluded volume is a state-dependent parameter. We also show that the dynamic properties of the systems plotted versus the ratio of the excluded to the free volumes determined by our approach collapsed onto one curve, which is in line with the isomorphs theory.

Anti-Kasha emissions of single molecules in a plasmonic nanocavity.

Kasha's rule generally holds true for solid-state molecular systems, where the rates of internal conversion and vibrational relaxation are sufficiently higher than the luminescence rate. In contrast, in systems where plasmons and matter interact strongly, the luminescence rate is significantly enhanced, leading to the emergence of luminescence that does not obey Kasha's rule. In this work, we investigate the anti-Kasha emissions of single molecules, free-base and magnesium naphthalocyanine (H2Nc and MgNc), in a plasmonic nanocavity formed between the tip of a scanning tunneling microscope (STM) and metal substrate. A narrow-line tunable laser was employed to precisely reveal the excited-state levels of a single molecule located under the tip and to selectively excite it into a specific excited state, followed by obtaining a STM-photoluminescence (STM-PL) spectrum to reveal the energy relaxation from the state. The excitation to higher-lying states of H2Nc caused various changes in the emission spectrum, such as broadening and the appearance of new peaks, implying the breakdown of Kasha's rule. These observations indicate emissions from the vibrationally excited states in the first singlet excited state (S1) and second singlet excited state (S2), as well as internal conversion from S2 to S1. Moreover, we obtained direct evidence of electronic and vibronic transitions from the vibrationally excited states, from the STM-PL measurements of MgNc. The results obtained herein shed light on the energy dynamics of molecular systems under a plasmonic field and highlight the possibility of obtaining various energy-converting functions using anti-Kasha processes.

Experimental and molecular dynamics simulation investigations of adhesion in heavy oil/water/pipeline wall systems during cold transportation

With the increased water content in heavy oil gathering pipelines and application of cold transportation methods, understanding adhesion behavior in heavy oil/water/pipeline wall systems has greatly gained in importance. Here, a combined experimental and molecular dynamics (MD) simulation was used to investigate the effect of the heavy oil composition and molecular structures on adhesion. The capacity of heavy oil adhesion and the effect of adding asphaltene molecules to the heavy oil were studied experimentally. MD simulation systems with different asphaltene concentrations were established based on the NPT and NVT ensembles. The results showed that the addition of asphaltene increases the adhesion of heavy oil by as much as 16.60–83.35%. The center of mass, mean square displacement, and radial distribution function of heavy oil droplets were also analyzed, which indicated that the viscosity and thickness of the heavy oil adhesion layer were also affected by the asphaltene content. The functional groups in asphaltene molecules promote the adhesion of oil droplets and inhibit the adhesion of the water phase. Further, asphaltene aggregates are formed by face-to-face stacking, which severely impedes heavy oil flow assurance. The results from this study greatly contribute to understanding the mechanisms of heavy oil adhesion during cold transportation.

A general method for the development of diabatic spin-orbit models for multi-electron systems.

Spin-orbit (SO) coupling can have significant effects on the quantum dynamics of molecular systems, but it is still difficult to account for accurately. One promising way to do this is to devise a diabatic SO model combined with the molecular potential energy. Few such models have been developed utilizing spatial and time-reversal symmetry. These models are tedious to derive and are specific for the molecular symmetry and included spin states. Here, we present a relatively simple approach to construct such models for various spin states with S≠12 from a basic one-electron SO case with S=12. The multi-electron fine structure states are expressed in terms of Slater determinants of single-electron spin functions (spinors). The properties of all single-electron matrix elements over the SO operator are derived and expressed as Taylor expansions in terms of symmetry-adapted nuclear coordinates. The SO matrix elements for the multi-electron case are then obtained from these single-electron matrix elements using the Slater-Condon rules. This yields the full SO matrix and symmetry properties of the multi-electron matrix elements in a straightforward way. The matrix elements are expressed as symmetry-adapted polynomials up to arbitrary order. This approach is demonstrated first for an abstract model of two electrons in a set of p orbitals in a C3v symmetric environment and then applied to set up a diabatic model for the photodissociation of methyl iodide (CH3I). The high accuracy of this new approach is demonstrated in comparison to an available analytic SO model for CH3I.

Recognition of the ligand-induced spatiotemporal residue pair pattern of β2-adrenergic receptors using 3-D residual networks trained by the time series of protein distance maps

G protein-coupled receptors (GPCRs) are promising drug targets because they play a large role in physiological processes by modulating diverse signaling pathways in the human body. The GPCR-mediated signaling pathways are regulated by four types of ligands—agonists, neutral antagonists, partial agonists, and inverse agonists. Once each type of ligand is bound to the binding site, it activates, deactivates, or does not perturb signaling by shifting the conformational ensemble of GPCRs. Predicting the ligand's effect on the conformation at the binding moment could be a powerful screening tool for rational GPCR drug design. Here, we detected conformational differences by capturing the spatiotemporal residue pair pattern of the ligand-bound β2-adrenergic receptor (β2AR) using a 3-dimensional residual network, 3D-ResNets. The network was trained with the time series of protein distance maps extracted from hundreds of molecular dynamics (MD) simulation trajectories of ten β2AR-ligand complexes. The MD system was constructed with a lipid bilayer embedded in an inactive β2AR X-ray crystal structure and solvated with explicit water molecules. To train the network, three hyperparameters were tested, and it was found that the number of MD trajectories in the training set significantly affected the model's accuracy. The classification of agonists and neutral antagonists was successful, but inverse agonists were not. Between the agonists and antagonists, different residue pair patterns were spotted on the extracellular loop segment. This result demonstrates the potential application of a 3-D neural network in GPCR drug screening, as well as an analysis tool for protein functional dynamics

Molecular dynamics analysis of biomolecular systems including nucleic acids.

With the recent progress in structural biology and genome biology, structural dynamics of molecular systems that include nucleic acids has attracted attention in the context of gene regulation. The structure-function relationship is an important topic that highlights the importance of the physicochemical properties of nucleotides, as well as that of amino acids in proteins. Simulations are a useful tool for the detailed analysis of molecular dynamics that complement experiments in molecular biology; however, molecular simulation of nucleic acids is less well developed than that of proteins partly due to the physical nature of nucleic acids. In this review, we briefly describe the current status and future directions of the field as a guide to promote collaboration between experimentalists and computational biologists.

Expansive Quantum Mechanical Exploration of Chemical Reaction Paths.

Quantum mechanical methods have been well-established for the elucidation of reaction paths of chemical processes and for the explicit dynamics of molecular systems. While they are usually deployed in routine manual calculations on reactions for which some insights are already available (typically from experiment), new algorithms and continuously increasing capabilities of modern computer hardware allow for exploratory open-ended computational campaigns that are unbiased and therefore enable unexpected discoveries. Highly efficient and even automated procedures facilitate systematic approaches toward the exploration of uncharted territory in molecular transformations and dynamics. In this work, we elaborate on such explorative approaches that range from reaction network explorations with (stationary) quantum chemical methods to explorative molecular dynamics and migrant wave packet dynamics. The focus is on recent developments that cover the following strategies. (i) Pruning search options for elementary reaction steps by heuristic rules based on the first-principles of quantum mechanics: Rules are required for reducing the combinatorial explosion of potentially reactive atom pairings, and rooting them in concepts derived from the electronic wave function makes them applicable to any molecular system. (ii) Enforcing reactive events by external biases: Inducing a reaction requires constraints that steer and direct elementary-step searches, which can be formulated in terms of forces, velocities, or supplementary potentials. (iii) Manual steering facilitated by interactive quantum mechanics: As ultrafast quantum chemical methods allow for real-time manual interactions with molecular systems, human-intuition-guided paths can be easily explored with suitable human-machine interfaces. (iv) New approaches for transition-state optimization with continuous curve representations can provide stable schemes to be driven in an automated way by allowing for an efficient tuning of the curve's parameters (instead of a manipulation of a collection of structures along the path), and (v) reactive molecular dynamics and direct wave packet propagation exploit the equations of motion of an underlying mechanical theory (usually, classical Newtonian mechanics or Schrödinger quantum mechanics). Explorative approaches are likely to replace the current state of the art in computational chemistry, because they reduce the human effort to be invested in reaction path elucidations, they are less prone to errors and bias-free, and they cover more extensive regions of the relevant configuration space. As a result, computational investigations that rely on these techniques are more likely to deliver surprising discoveries.

Analyzing Grid-Based Direct Quantum Molecular Dynamics Using Non-Linear Dimensionality Reduction.

Grid-based schemes for simulating quantum dynamics, such as the multi-configuration time-dependent Hartree (MCTDH) method, provide highly accurate predictions of the coupled nuclear and electronic dynamics in molecular systems. Such approaches provide a multi-dimensional, time-dependent view of the system wavefunction represented on a coordinate grid; in the case of non-adiabatic simulations, additional information about the state populations adds a further layer of complexity. As such, wavepacket motion on potential energy surfaces which couple many nuclear and electronic degrees-of-freedom can be extremely challenging to analyse in order to extract physical insight beyond the usual expectation-value picture. Here, we show that non-linear dimensionality reduction (NLDR) methods, notably diffusion maps, can be adapted to extract information from grid-based wavefunction dynamics simulations, providing insight into key nuclear motions which explain the observed dynamics. This approach is demonstrated for 2-D and 9-D models of proton transfer in salicylaldimine, as well as 8-D and full 12-D simulations of cis-trans isomerization in ethene; these simulations demonstrate how NLDR can provide alternative views of wavefunction dynamics, and also highlight future developments.