Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.

Parallel algorithms for hyperdynamics and local hyperdynamics.

Hyperdynamics (HD) is a method for accelerating the timescale of standard molecular dynamics (MD). It can be used for simulations of systems with an energy potential landscape that is a collection of basins, separated by barriers, where transitions between basins are infrequent. HD enables the system to escape from a basin more quickly while enabling a statistically accurate renormalization of the simulation time, thus effectively boosting the timescale of the simulation. In the work of Kim et al. [J. Chem. Phys. 139, 144110 (2013)], a local version of HD was formulated, which exploits the intrinsic locality characteristic typical of most systems to mitigate the poor scaling properties of standard HD as the system size is increased. Here, we discuss how both HD and local HD can be formulated to run efficiently in parallel. We have implemented these ideas in the LAMMPS MD code, which means HD can be used with any interatomic potential LAMMPS supports. Together, these parallel methods allow simulations of any size to achieve the time acceleration offered by HD (which can be orders of magnitude), at a cost of 2-4× that of standard MD. As examples, we performed two simulations of a million-atom system to model the diffusion and clustering of Pt adatoms on a large patch of the Pt(100) surface for 80 µs and 160 µs.

Computing long time scale biomolecular dynamics using quasi-stationary distribution kinetic Monte Carlo (QSD-KMC).

It is a challenge to obtain an accurate model of the state-to-state dynamics of a complex biological system from molecular dynamics (MD) simulations. In recent years, Markov state models have gained immense popularity for computing state-to-state dynamics from a pool of short MD simulations. However, the assumption that the underlying dynamics on the reduced space is Markovian induces a systematic bias in the model, especially in biomolecular systems with complicated energy landscapes. To address this problem, we have devised a new approach we call quasistationary distribution kinetic Monte Carlo (QSD-KMC) that gives accurate long time state-to-state evolution while retaining the entire time resolution even when the dynamics is highly non-Markovian. The proposed method is a kinetic Monte Carlo approach that takes advantage of two concepts: (i) the quasistationary distribution, the distribution that results when a trajectory remains in one state for a long time (the dephasing time), such that the next escape is Markovian, and (ii) dynamical corrections theory, which properly accounts for the correlated events that occur as a trajectory passes from state to state before it settles again. In practice, this is achieved by specifying, for each escape, the intermediate states and the final state that has resulted from the escape. Implementation of QSD-KMC imposes stricter requirements on the lengths of the trajectories than in a Markov state model approach as the trajectories must be long enough to dephase. However, the QSD-KMC model produces state-to-state trajectories that are statistically indistinguishable from an MD trajectory mapped onto the discrete set of states for an arbitrary choice of state decomposition. Furthermore, the aforementioned concepts can be used to construct a Monte Carlo approach to optimize the state boundaries regardless of the initial choice of states. We demonstrate the QSD-KMC method on two one-dimensional model systems, one of which is a driven nonequilibrium system, and on two well-characterized biomolecular systems.

An Overview of Recent Standard and Accelerated Molecular Dynamics Simulations of Helium Behavior in Tungsten.

One of the most critical challenges for the successful adoption of nuclear fusion power corresponds to plasma-facing materials. Due to its favorable properties in this context (low sputtering yield, high thermal conductivity, high melting point, among others), tungsten is a leading candidate material. Nevertheless, tungsten is affected by the plasma and fusion byproducts. Irradiation by helium nuclei, in particular, strongly modifies the surface structure by a synergy of processes, whose origin is the nucleation and growth of helium bubbles. In this review, we present recent advances in the understanding of helium effects in tungsten from a simulational approach based on accelerated molecular dynamics, which emphasizes the use of realistic parameters, as are expected in experimental and operational fusion power conditions.

Langevin synchronization in a time-dependent, harmonic basin: An exact solution in 1D.

The trajectories of two particles undergoing Langevin dynamics while sharing a common noise sequence can merge into a single (master) trajectory. Here, we present an exact solution for a particle undergoing Langevin dynamics in a harmonic, time-dependent potential, thus extending the idea of synchronization to nonequilibrium systems. We calculate the synchronization level, i.e., the mismatch between two trajectories sharing a common noise sequence, in the underdamped, critically damped, and overdamped regimes. Finally, we provide asymptotic expansions in various limiting cases and compare to the time independent case.

Cluster analysis of accelerated molecular dynamics simulations: A case study of the decahedron to icosahedron transition in Pt nanoparticles.

Modern molecular-dynamics-based techniques are extremely powerful to investigate the dynamical evolution of materials. With the increase in sophistication of the simulation techniques and the ubiquity of massively parallel computing platforms, atomistic simulations now generate very large amounts of data, which have to be carefully analyzed in order to reveal key features of the underlying trajectories, including the nature and characteristics of the relevant reaction pathways. We show that clustering algorithms, such as the Perron Cluster Cluster Analysis, can provide reduced representations that greatly facilitate the interpretation of complex trajectories. To illustrate this point, clustering tools are used to identify the key kinetic steps in complex accelerated molecular dynamics trajectories exhibiting shape fluctuations in Pt nanoclusters. This analysis provides an easily interpretable coarse representation of the reaction pathways in terms of a handful of clusters, in contrast to the raw trajectory that contains thousands of unique states and tens of thousands of transitions.

Evidence for percolation diffusion of cations and reordering in disordered pyrochlore from accelerated molecular dynamics.

Diffusion in complex oxides is critical to ionic transport, radiation damage evolution, sintering, and aging. In complex oxides such as pyrochlores, anionic diffusion is dramatically affected by cation disorder. However, little is known about how disorder influences cation transport. Here, we report results from classical and accelerated molecular dynamics simulations of vacancy-mediated cation diffusion in Gd2Ti2O7 pyrochlore, on the microsecond timescale. We find that diffusion is slow at low levels of disorder, while higher disorder allows for fast diffusion, which is then accompanied by antisite annihilation and reordering, and thus a slowing of cation transport. Cation diffusivity is therefore not constant, but decreases as the material reorders. We also show that fast cation diffusion is triggered by the formation of a percolation network of antisites. This is in contrast with observations from other complex oxides and disordered media models, suggesting a fundamentally different relation between disorder and mass transport.Diffusion plays an important role in sintering, damage tolerance and transport. Here authors perform classical and accelerated molecular dynamics simulations of vacancy-mediated cation diffusion in Gd2Ti2O7 pyrochlore and report non-monotonic evolution of cation diffusivity.

The mobility of small vacancy/helium complexes in tungsten and its impact on retention in fusion-relevant conditions.

Tungsten is a promising plasma facing material for fusion reactors. Despite many favorable properties, helium ions incoming from the plasma are known to dramatically affect the microstructure of tungsten, leading to bubble growth, blistering, and/or to the formation of fuzz. In order to develop mitigation strategies, it is essential to understand the atomistic processes that lead to bubble formation and subsequent microstructural changes. In this work, we use large-scale Accelerated Molecular Dynamics simulations to investigate small (N = 1,2) V N He M vacancy/helium complexes, which serve as the nuclei for larger helium bubble growth, over timescales reaching into the milliseconds under conditions typical of the operation of fusion reactors. These complexes can interconvert between different I L V N+L He M variants via Frenkel pair nucleation (leading to the creation of a additional vacancy/interstitial pair) and annihilation events; sequences of these events can lead to net migration of these embryonic bubbles. The competition between nucleation and annihilation produces a very complex dependence of the diffusivity on the number of heliums. Finally, through cluster dynamics simulations, we show that diffusion of these complexes provides an efficient pathway for helium release at fluxes expected in fusion reactors, and hence that accounting for the mobility of these complexes is crucial.

Accelerating ring-polymer molecular dynamics with parallel-replica dynamics.

Nuclear quantum effects are important for systems containing light elements, and the effects are more prominent in the low temperature regime where the dynamics also becomes sluggish. We show that parallel replica (ParRep) dynamics, an accelerated molecular dynamics approach for infrequent-event systems, can be effectively combined with ring-polymer molecular dynamics, a semiclassical trajectory approach that gives a good approximation to zero-point and tunneling effects in activated escape processes. The resulting RP-ParRep method is a powerful tool for reaching long time scales in complex infrequent-event systems where quantum dynamics are important. Two illustrative examples, symmetric Eckart barrier crossing and interstitial helium diffusion in Fe and Fe-Cr alloy, are presented to demonstrate the accuracy and long-time scale capability of this approach.

The effects of cation-anion clustering on defect migration in MgAl2O4.

Magnesium aluminate spinel (MgAl2O4), like many other ceramic materials, offers a range of technological applications, from nuclear reactor materials to military body armor. For many of these applications, it is critical to understand both the formation and evolution of lattice defects throughout the lifetime of the material. We use the Speculatively Parallel Temperature Accelerated Dynamics (SpecTAD) method to investigate the effects of di-vacancy and di-interstitial formation on the mobility of the component defects. From long-time trajectories of the state-to-state dynamics, we characterize the migration pathways of defect clusters, and calculate their self-diffusion constants across a range of temperatures. We find that the clustering of Al and O vacancies drastically reduces the mobility of both defects, while the clustering of Mg and O vacancies completely immobilizes them. For interstitials, we find that the clustering of Mg and O defects greatly reduces O interstitial mobility, but has only a weak effect on Mg. These findings illuminate important new details regarding defect kinetics relevant to the application of MgAl2O4 in extreme environments.

The Modern Temperature-Accelerated Dynamics Approach.

Accelerated molecular dynamics (AMD) is a class of MD-based methods used to simulate atomistic systems in which the metastable state-to-state evolution is slow compared with thermal vibrations. Temperature-accelerated dynamics (TAD) is a particularly efficient AMD procedure in which the predicted evolution is hastened by elevating the temperature of the system and then recovering the correct state-to-state dynamics at the temperature of interest. TAD has been used to study various materials applications, often revealing surprising behavior beyond the reach of direct MD. This success has inspired several algorithmic performance enhancements, as well as the analysis of its mathematical framework. Recently, these enhancements have leveraged parallel programming techniques to enhance both the spatial and temporal scaling of the traditional approach. We review the ongoing evolution of the modern TAD method and introduce the latest development: speculatively parallel TAD.

Long-Time Dynamics through Parallel Trajectory Splicing.

Simulating the atomistic evolution of materials over long time scales is a longstanding challenge, especially for complex systems where the distribution of barrier heights is very heterogeneous. Such systems are difficult to investigate using conventional long-time scale techniques, and the fact that they tend to remain trapped in small regions of configuration space for extended periods of time strongly limits the physical insights gained from short simulations. We introduce a novel simulation technique, Parallel Trajectory Splicing (ParSplice), that aims at addressing this problem through the timewise parallelization of long trajectories. The computational efficiency of ParSplice stems from a speculation strategy whereby predictions of the future evolution of the system are leveraged to increase the amount of work that can be concurrently performed at any one time, hence improving the scalability of the method. ParSplice is also able to accurately account for, and potentially reuse, a substantial fraction of the computational work invested in the simulation. We validate the method on a simple Ag surface system and demonstrate substantial increases in efficiency compared to previous methods. We then demonstrate the power of ParSplice through the study of topology changes in Ag42Cu13 core-shell nanoparticles.

Hyperdynamics boost factor achievable with an ideal bias potential.

Hyperdynamics is a powerful method to significantly extend the time scales amenable to molecular dynamics simulation of infrequent events. One outstanding challenge, however, is the development of the so-called bias potential required by the method. In this work, we design a bias potential using information about all minimum energy pathways (MEPs) out of the current state. While this approach is not suitable for use in an actual hyperdynamics simulation, because the pathways are generally not known in advance, it allows us to show that it is possible to come very close to the theoretical boost limit of hyperdynamics while maintaining high accuracy. We demonstrate this by applying this MEP-based hyperdynamics (MEP-HD) to metallic surface diffusion systems. In most cases, MEP-HD gives boost factors that are orders of magnitude larger than the best existing bias potential, indicating that further development of hyperdynamics bias potentials could have a significant payoff. Finally, we discuss potential practical uses of MEP-HD, including the possibility of developing MEP-HD into a true hyperdynamics.

Thermostating extended Lagrangian Born-Oppenheimer molecular dynamics.

Extended Lagrangian Born-Oppenheimer molecular dynamics is developed and analyzed for applications in canonical (NVT) simulations. Three different approaches are considered: the Nosé and Andersen thermostats and Langevin dynamics. We have tested the temperature distribution under different conditions of self-consistent field (SCF) convergence and time step and compared the results to analytical predictions. We find that the simulations based on the extended Lagrangian Born-Oppenheimer framework provide accurate canonical distributions even under approximate SCF convergence, often requiring only a single diagonalization per time step, whereas regular Born-Oppenheimer formulations exhibit unphysical fluctuations unless a sufficiently high degree of convergence is reached at each time step. The thermostated extended Lagrangian framework thus offers an accurate approach to sample processes in the canonical ensemble at a fraction of the computational cost of regular Born-Oppenheimer molecular dynamics simulations.

Competing kinetics and he bubble morphology in W.

The growth process of He bubbles in W is investigated using molecular dynamics and parallel replica dynamics for growth rates spanning 6 orders of magnitude. Fast and slow growth regimes are defined relative to typical diffusion hopping times of W interstitials around the He bubble. Slow growth rates allow the diffusion of interstitials around the bubble, favoring the biased growth of the bubble towards the surface. In contrast, at fast growth rates interstitials do not have time to diffuse around the bubble, leading to a more isotropic growth and increasing the surface damage.

The relationship between grain boundary structure, defect mobility, and grain boundary sink efficiency.

Nanocrystalline materials have received great attention due to their potential for improved functionality and have been proposed for extreme environments where the interfaces are expected to promote radiation tolerance. However, the precise role of the interfaces in modifying defect behavior is unclear. Using long-time simulations methods, we determine the mobility of defects and defect clusters at grain boundaries in Cu. We find that mobilities vary significantly with boundary structure and cluster size, with larger clusters exhibiting reduced mobility, and that interface sink efficiency depends on the kinetics of defects within the interface via the in-boundary annihilation rate of defects. Thus, sink efficiency is a strong function of defect mobility, which depends on boundary structure, a property that evolves with time. Further, defect mobility at boundaries can be slower than in the bulk, which has general implications for the properties of polycrystalline materials. Finally, we correlate defect energetics with the volumes of atomic sites at the boundary.

Sublattice parallel replica dynamics.

Exascale computing presents a challenge for the scientific community as new algorithms must be developed to take full advantage of the new computing paradigm. Atomistic simulation methods that offer full fidelity to the underlying potential, i.e., molecular dynamics (MD) and parallel replica dynamics, fail to use the whole machine speedup, leaving a region in time and sample size space that is unattainable with current algorithms. In this paper, we present an extension of the parallel replica dynamics algorithm [A. F. Voter, Phys. Rev. B 57, R13985 (1998)] by combining it with the synchronous sublattice approach of Shim and Amar [ and , Phys. Rev. B 71, 125432 (2005)], thereby exploiting event locality to improve the algorithm scalability. This algorithm is based on a domain decomposition in which events happen independently in different regions in the sample. We develop an analytical expression for the speedup given by this sublattice parallel replica dynamics algorithm and compare it with parallel MD and traditional parallel replica dynamics. We demonstrate how this algorithm, which introduces a slight additional approximation of event locality, enables the study of physical systems unreachable with traditional methodologies and promises to better utilize the resources of current high performance and future exascale computers.

Extending atomistic simulation timescale in solid/liquid systems: crystal growth from solution by a parallel-replica dynamics and continuum hybrid method.

Deposition of solid material from solution is ubiquitous in nature. However, due to the inherent complexity of such systems, this process is comparatively much less understood than deposition from a gas or vacuum. Further, the accurate atomistic modeling of such systems is computationally expensive, therefore leaving many intriguing long-timescale phenomena out of reach. We present an atomistic/continuum hybrid method for extending the simulation timescales of dynamics at solid/liquid interfaces. We demonstrate the method by simulating the deposition of Ag on Ag (001) from solution with a significant speedup over standard MD. The results reveal specific features of diffusive deposition dynamics, such as a dramatic increase in the roughness of the film.

Local hyperdynamics.

We present a new formulation of the hyperdynamics method in which the biasing effect is local, making it suitable for large systems. In standard hyperdynamics, the requirement that the bias potential be zero everywhere on the dividing surface bounding the state has the consequence that as the system size increases the boost factor decays to unity, regardless of the form of the bias potential. In the new method, the bias force on each atom is obtained by differentiating a local bias energy that depends only on the coordinates of atoms within a finite range of this atom. This bias force is thus independent of the bias force in distant parts of the system, providing a method that gives a constant boost factor, independent of the system size. We demonstrate for some realistic atomistic systems that the method gives escape rates in excellent agreement with direct molecular dynamics simulations.

Influence of point defects on grain boundary mobility in bcc tungsten.

Atomistic computer simulations were performed to study the influence of radiation-induced damage on grain boundary (GB) sliding processes in bcc tungsten (W), the divertor material in the ITER tokamak and the leading candidate for the first wall material in future fusion reactors. In particular, we calculated the average sliding-friction force as a function of the number of point defects introduced into the GB for a number of symmetric tilt GBs. In all cases the average sliding-friction force at fixed shear strain rate depends on the number of point defects introduced into the GB, and in many cases introduction of these defects reduces the average sliding-friction force by roughly an order of magnitude. We have also observed that as the number of interstitials in the GB is varied, the direction of the coupled GB motion sometimes reverses, causing the GB to migrate in the opposite direction under the same applied shear stress. This could be important in the microstructural evolution of polycrystalline W under the harsh radiation environment in a fusion reactor, in which high internal stresses are present and frequent collision cascades generate interstitials and vacancies.