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The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

During the activation of mitogen-activated protein kinase (MAPK) signaling, the RAS-binding domain (RBD) and cysteine-rich domain (CRD) of RAF bind to active RAS at the plasma membrane. The orientation of RAS at the membrane may be critical for formation of the RAS-RBDCRD complex and subsequent signaling. To explore how RAS membrane orientation relates to the protein dynamics within the RAS-RBDCRD complex, we perform multiscale coarse-grained and all-atom molecular dynamics (MD) simulations of KRAS4b bound to the RBD and CRD domains of RAF-1, both in solution and anchored to a model plasma membrane. Solution MD simulations describe dynamic KRAS4b-CRD conformations, suggesting that the CRD has sufficient flexibility in this environment to substantially change its binding interface with KRAS4b. In contrast, when the ternary complex is anchored to the membrane, the mobility of the CRD relative to KRAS4b is restricted, resulting in fewer distinct KRAS4b-CRD conformations. These simulations implicate membrane orientations of the ternary complex that are consistent with NMR measurements. While a crystal structure-like conformation is observed in both solution and membrane simulations, a particular intermolecular rearrangement of the ternary complex is observed only when it is anchored to the membrane. This configuration emerges when the CRD hydrophobic loops are inserted into the membrane and helices α3-5 of KRAS4b are solvent exposed. This membrane-specific configuration is stabilized by KRAS4b-CRD contacts that are not observed in the crystal structure. These results suggest modulatory interplay between the CRD and plasma membrane that correlate with RAS/RAF complex structure and dynamics, and potentially influence subsequent steps in the activation of MAPK signaling.

Significance Here we present an unprecedented multiscale simulation platform that enables modeling, hypothesis generation, and discovery across biologically relevant length and time scales to predict mechanisms that can be tested experimentally. We demonstrate that our predictive simulation-experimental validation loop generates accurate insights into RAS-membrane biology. Evaluating over 100,000 correlated simulations, we show that RAS–lipid interactions are dynamic and evolving, resulting in: 1) a reordering and selection of lipid domains in realistic eight-lipid bilayers, 2) clustering of RAS into multimers correlating with specific lipid fingerprints, 3) changes in the orientation of the RAS G-domain impacting its ability to interact with effectors, and 4) demonstration that RAS–RAS G-domain interfaces are nonspecific in these putative signaling domains., 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.

We have implemented the Martini force field within Lawrence Livermore National Laboratory’s molecular dynamics program, ddcMD. The program is extended to a heterogeneous programming model so that it can exploit graphics processing unit (GPU) accelerators. In addition to the Martini force field being ported to the GPU, the entire integration step, including thermostat, barostat, and constraint solver, is ported as well, which speeds up the simulations to 278-fold using one GPU vs one central processing unit (CPU) core. A benchmark study is performed with several test cases, comparing ddcMD and GROMACS Martini simulations. The average performance of ddcMD for a protein–lipid simulation system of 136k particles achieves 1.04 µs/day on one NVIDIA V100 GPU and aggregates 6.19 µs/day on one Summit node with six GPUs. The GPU implementation in ddcMD offloads all computations to the GPU and only requires one CPU core per simulation to manage the inputs and outputs, freeing up remaining CPU resources on the compute node for alternative tasks often required in complex simulation campaigns. The ddcMD code has been made open source and is available on GitHub at https://github.com/LLNL/ddcMD.

The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data including: molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next generation high performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and eventually prescribe specialized treatments for patients with cancer.

Simulating proteins on lipid membranes could provide unprecedented insights into cancer biology and a host of other phenomena. However, such simulations face conflicting computational constraints: reaching biologically relevant time and length scales (milliseconds and microns) requires continuum-level models but understanding the processes of interest requires molecular level detail. I will present a new type of massively parallel, multiscale simulation framework that brings together these two modeling paradigms. Using a machine learning algorithm, we couple a novel continuum model to an ensemble of molecular dynamics simulations. The result is a simulation at macro length- and time-scales that incorporates microscale precision. We demonstrate this capability with a large-scale simulation of several hundred k-Ras proteins on a realistic lipid bilayer spanning 1 μm2. Citation Format: Frederick H. Streitz. Multiscale simulations of k-Ras on lipid bilayers [abstract]. In: Proceedings of the AACR Special Conference on Targeting RAS-Driven Cancers; 2018 Dec 9-12; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(5_Suppl):Abstract nr IA10.

Driver mutations in KRAS occur in almost 30% of human tumors, primarily in pancreatic, colorectal and lung tumors. These mutations result in increased cell proliferation and survival predominantly mediated through the MAPK signaling pathway. MAPK signal transduction is initiated by the interaction of RAF kinase with active RAS at the plasma membrane. The precise molecular details of this process are currently unknown. The Frederick National Laboratory for Cancer Research has partnered with the Department of Energy to harness high-performance computing and experimental data to generate models and hypotheses of how KRAS engages with RAF kinase at the plasma membrane to initiate signal transduction. The initial phase of this work has focused on identifying membrane bound states of KRAS. We have used a variety of biophysical approaches (including NMR, protein foot-printing and neutron reflectivity) to investigate the structural orientation of KRAS at the membrane. In addition, large scale coarse-grained simulations of membrane bound KRAS spanning the millisecond time scale, have been completed. Three predominant membrane bound KRAS states were observed computationally: an exposed state (where switch 1 is available for RAF binding), an occluded state (where switch 1 is unavailable for RAF binding) and a transition state (where helix 5 is perpendicular to the membrane). These three states are also identified in the experimental data. Cumulatively, experimental and computational data predict KRAS exists in a dynamic equilibrium on the plasma membrane, interconverting between 3 states on the nanosecond time scale. The experimental data indicates the most populated conformation of KRAS is the transition state. Future efforts will address the significance of these three states for RAF interaction and signal transduction. This in depth understanding of RAS activation of RAF and the MAPK pathway is critically important for developing effective therapeutic interventions for cancers harboring mutant RAS. Citation Format: Andrew G. Stephen, Animesh Agarwal, Angel E. Garcia, Gnana S. Gnanakaran, Jeevapani Hettige, Christopher Neale, Timothy Travers, Harsh Bhatia, Peer-Timo Bremer, Tim Carpenter, Jim Glosli, Helgi Ingolfsson, Piyush Karande, Felice Lightstone, Tomas Oppelstrup, Liam Stanton, Shiv Sundram, Xiaohua Zhang, Debsindhu Bhowmik, Arvind Ramanathan, Christopher Stanley, Debanjan Goswami, Gulcin Gulten, Frantz Jean-Francios, Dhirendra Simanshu, Tommy Turbyville, Rebika Shrestha, Que Van, Frank McCormick, Dwight Nissley, Fred Streitz, Constance Agamasu. Identification of KRAS membrane bound states using an integrated computational and experimental approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3373.

We describe the status of a new time-dependent simulation capability for dense plasmas. The backbone of this multi-institutional effort – the Cimarron Project – is the massively parallel molecular dynamics (MD) code “ddcMD,” developed at Lawrence Livermore National Laboratory. The project’s focus is material conditions such as exist in inertial confinement fusion experiments, and in many stellar interiors: high temperatures, high densities, significant electromagnetic fields, mixtures of high- and low-Z elements, and non-Maxwellian particle distributions. Of particular importance is our ability to incorporate into this classical MD code key atomic, radiative, and nuclear processes, so that their interacting effects under non-ideal plasma conditions can be investigated. This paper summarizes progress in computational methodology, discusses strengths and weaknesses of quantum statistical potentials as effective interactions for MD, explains the model used for quantum events possibly occurring in a collision, describes two new experimental efforts that play a central role in our validation work, highlights some significant results obtained to date, outlines concepts now being explored to deal more efficiently with the very disparate dynamical timescales that arise in fusion plasmas, and provides a careful comparison of quantum effects on electron trajectories predicted by more elaborate dynamical methods.

We investigate solidification in metal systems ranging in size from 64,000 to 524,288,000 atoms on the IBM BlueGene/L computer at LLNL. Using the newly developed ddcMD code, we achieve performance rates as high as 103 TFlops, with a performance of 101.7 TFlop sustained over a 7 hour run on 131,072 cpus. We demonstrate superb strong and weak scaling. Our calculations are significant as they represent the first atomic-scale model of metal solidification to proceed, without finite size effects, from spontaneous nucleation and growth of solid out of the liquid, through the coalescence phase, and into the onset of coarsening. Thus, our simulations represent the first step towards an atomistic model of nucleation and growth that can directly link atomistic to mesoscopic length scales.

First-principles generalized pseudopotential theory (GPT) provides a fundamental basis for transferable multi-ion interatomic potentials in transition metals and alloys within density-functional quantum mechanics. In the central body-centered cubic (bcc) metals, where multi-ion angular forces are important to materials properties, simplified model GPT (MGPT) potentials have been developed based on canonical d bands to allow analytic forms and large-scale atomistic simulations. Robust, advanced-generation MGPT potentials have now been obtained for Ta and Mo and successfully applied to a wide range of structural, thermodynamic, defect, and mechanical properties at both ambient and extreme conditions. Selected applications to multiscale modeling discussed here include dislocation core structure and mobility, atomistically informed dislocation dynamics simulations of plasticity, and thermoelasticity and high-pressure strength modeling. Recent algorithm improvements have provided a more general matrix representation of MGPT beyond canonical bands, allowing improved accuracy and extension to f-electron actinide metals, an order of magnitude increase in computational speed for dynamic simulations, and the development of temperature-dependent potentials.

We present an overview of recent work on quantum-based atomistic simulation of materials properties in transition metals performed in the Metals and Alloys Group at Lawrence Livermore National Laboratory. Central to much of this effort has been the development, from fundamental quantum mechanics, of robust many-body interatomic potentials for bcc transition metals via model generalized pseudopotential theory (MGPT), providing close linkage between ab?initio electronic-structure calculations and large-scale static and dynamic atomistic simulations. In the case of tantalum (Ta), accurate MGPT potentials have been so obtained that are applicable to structural, thermodynamic, defect, and mechanical properties over wide ranges of pressure and temperature. Successful application areas discussed include structural phase stability, equation of state, melting, rapid resolidification, high-pressure elastic moduli, ideal shear strength, vacancy and self-interstitial formation and migration, grain-boundary atomic structure, and dislocation core structure and mobility. A number of the simulated properties allow detailed validation of the Ta potentials through comparisons with experiment and/or parallel electronic-structure calculations. Elastic and dislocation properties provide direct input into higher-length-scale multiscale simulations of plasticity and strength. Corresponding effort has also been initiated on the multiscale materials modelling of fracture and failure. Here large-scale atomistic simulations and novel real-time characterization techniques are being used to study void nucleation, growth, interaction, and coalescence in series-end fcc transition metals. We have so investigated the microscopic mechanisms of void nucleation in polycrystalline copper (Cu), and void growth in single-crystal and polycrystalline Cu, undergoing triaxial expansion at a large, constant strain rate - a process central to the initial phase of dynamic fracture. The influence of pre-existing microstructure on the void growth has been characterized both for nucleation and for growth, and these processes are found to be in agreement with the general features of void distributions observed in experiment. We have also examined some of the microscopic mechanisms of plasticity associated with void growth.

We present results for total-energy simulations of the energetics of vacancy occupation of \ensuremath{\gamma} alumina using an empirical potential that allows the ionic valence to vary according to the local environment and includes Coulombic electrostatic interactions among the anions and cations. Our model predicts that although the minimum enthalpy configuration has all aluminum vacancies from the spinel structure in octahedral sites, the energetic cost of a modest fraction of tetrahedral sites is small. We furthermore use our model to explain the differences between previously reported simulations on this question in terms of their constraints on structural and charge relaxation.

We use molecular dynamics (MD) to simulate diffusion in molten aluminum-copper (AlCu) alloys. The self-diffusivities and Maxwell-Stefan diffusivities are calculated for AlCu mixtures using the Green-Kubo formulas at temperatures from 1000 to 4000 K and pressures from 0 to 25 GPa, along with additional points at higher temperatures and pressures. The diffusivities are corrected for finite-size effects. The Maxwell-Stefan diffusivity is compared to the diffusivity calculated from the self-diffusivities using a generalization of the Darken equation. We find that the effects of cross-correlation are small. Using the calculated self-diffusivities, we have assessed whether dilute hard-sphere and dilute Lennard-Jones models apply to the molten mixture. Neither of the two dilute gas diffusivities describes the diffusivity in molten Al and Cu. We report generalized analytic models for the self-diffusivities and interdiffusivity (mutual diffusivity) that fit the MD results well. The MD-derived transport coefficients are in good agreement with the available experimental data. We also report MD calculations of the viscosity and an analytic fit to those results. The ionic thermal conductivity is discussed briefly.

As most technologically important metals will form oxides readily, any complete study of adhesion at real metal surfaces must include the metal-oxide interface. The role of this ubiquitous oxide layer cannot be overlooked, as the adhesive properties of the oxide or oxide-metal system can be expressed to differ profoundly from the adhesive properties of a bare metal surface. We report on the development of a novel computational method for molecular dynamics simulations which explicitly includes variable charge transfer between anions and cations. This method is found to be capable of describing the elastic properties, surface energies, and surface relaxation of crystalline metal-oxides accurately. We discuss in detail results using this method of α-alumina and several of its low index faces.

We report on the development of a novel computational method for molecular dynamics simulations which explicitly includes variable charge transfer between anions and cations. This method is found to be capable of describing the elastic properties, surface energies, and surface relaxation of crystalline metal-oxides accurately. We present results for a simulation of adhesive failure at a model metal/oxide heterophase interface between an aluminum (111) face and an α-alumina (0001) face. Our results indicate that this approach can provide physically realistic empirical potentials for future simulations on mixed metal/metal-oxide systems.

We discuss the development of interaction potentials which explicitly allow for charge transfer in metallic oxides. The charge transfer is calculated self-consistently using a charge equilibration approach, which allows the amount of charge transferred to respond to the electrostatic environment. We model the metal-metal, metal-oxygen, and oxygen-oxygen interactions with Rydberg function pair potentials. By fitting the Rydberg potential parameters to the elastic and structural constants of the material, we arrive at an efficient model for the simulation of metallic oxides. We demonstrate the applicability of the model by describing some preliminary results on the rutile phase of titanium dioxide.

We report on the results of molecular-dynamic simulations of vacancy and vacancy-step interactions on a (111) Au surface. Near the vacancy the strain field decays as ${\mathit{r}}^{\mathrm{\ensuremath{-}}\mathit{n}}$ with n\ensuremath{\sim}2 and the dilatational strain undergoes a rapid oscillatory decay to zero at a distance of 1 nm from the vacancy. This result suggests that there is no direct long-range elastic interaction between vacancies on this surface. The elastic interaction between a vacancy and a step only becomes significant at separations of less than \ensuremath{\sim}1 nm and is controlled by a mixing of the oscillatory pressure field of the step with the dilatational field of the vacancy. Our calculations indicate that a vacancy on the upper portion of a terrace associated with a step has to overcome an energy barrier of \ensuremath{\sim}0.1 eV in order for vacancy annihilation to occur at the step edge.

We use classical molecular dynamics to investigate electron-ion temperature equilibration in a two-temperature SF{sub 6} plasma. We choose a density of 1.0x10{sup 19}SF{sub 6} molecules per cm{sup 3} and initial temperatures of T{sub e}=100 eV and T{sub S}=T{sub F}=15 eV, in accordance with experiments currently underway at Los Alamos National Laboratory. Our computed relaxation time lies between two oft-used variants of the Landau-Spitzer relaxation formula which invoke static screening. Discrepancies are also found when comparing to the predictions made by more recent theoretical approaches. These differences should be large enough to be measured in the upcoming experiments.

The temperature equilibration rate between electrons and protons in dense hydrogen has been calculated with molecular dynamics simulations for temperatures between 10 and 600eV and densities between 10;{20}cm;{-3}to10;{24}cm;{-3} . Careful attention has been devoted to convergence of the simulations, including the role of semiclassical potentials. We find that for Coulomb logarithms L greater, similar1 , a model by Gericke-Murillo-Schlanges (GMS) [D. O. Gericke, Phys. Rev. E 65, 036418 (2002)] based on a T -matrix method and the approach by Brown-Preston-Singleton [L. S. Brown, Phys. Rep. 410, 237 (2005)] agrees with the simulation data to within the error bars of the simulation. For smaller Coulomb logarithms, the GMS model is consistent with the simulation results. Landau-Spitzer models are consistent with the simulation data for L4 .

Although computer simulation has played a central role in the study of nucleation and growth since the earliest molecular dynamics simulations almost 50 years ago, confusion surrounding the effect of finite size on such simulations have limited their applicability. Modeling solidification in molten tantalum on the BlueGene/L computer, we report here on the first atomistic simulation of solidification that verifies independence from finite size effects during the entire nucleation and growth process, up to the onset of coarsening. We show that finite size scaling theory explains the observed maximal grain sizes for systems up to about 8,000,000 atoms. For larger simulations, a cross-over from finite size scaling to more physical size-independent behavior is observed.

First‐principles generalized pseudopotential theory (GPT) provides a fundamental basis for transferable multi‐ion interatomic potentials in d‐electron transition metals within density‐functional quantum mechanics. In mid‐period bcc metals, where multi‐ion angular forces are important to structural properties, simplified model GPT or MGPT potentials have been developed based on canonical d bands to allow analytic forms and large‐scale atomistic simulations. Robust, advanced‐generation MGPT potentials have now been obtained for Ta and Mo and successfully applied to a wide range of structural, thermodynamic, defect and mechanical properties at both ambient and extreme conditions of pressure and temperature. Recent algorithm improvements have also led to a more general matrix representation of MGPT beyond canonical bands allowing increased accuracy and extension to f‐electron actinide metals, an order of magnitude increase in computational speed, and the current development of temperature‐dependent potentials.

We calculate the optical-absorption spectrum of solid and liquid Al for temperatures between 0 and 1800 K with an empirical pseudopotential technique. It is found that a prominent peak in the spectrum of the solid !at !"#1.5 eV" smoothly disappears as the temperature is raised above Tmelt=938 K. This is in agreement with earlier measurements on solid and liquid Al, but is in disagreement with more recent measurements on the liquid, in which the peak was seen to remain at T=1550 K. Our results suggest that in situ optical constants measurements may be used as a diagnostic for the shock melting of Al.

We describe here a series of dynamic compression experiments using impactors with specifically prescribed density profiles. Building upon previous impactor designs, we compose our functionally graded density impactors of materials whose densities vary from about 0.1 g/cc to more than 15 g/cc. These impactors, whose density profiles are not restricted to be monotonic, can be used to generate prescribed thermodynamic paths in the targets. These paths include quasi-isentropes as well as combinations of shock, rarefraction, and quasi-isentropic compression waves. The time-scale of these experiments ranges from nanoseconds to several microseconds. Strain-rates in the quasi-isentropic compression experiments vary from approximately 10{sup 4}s{sup -1} to 10{sup 6}s{sup -1}. We applied this quasi-isentropic compression technique to resolidify water where ice is at a higher temperature than the initial water sample. The particle velocity of quasi-isentropically compressed water exhibits a two-wave structure and sample thickness scales consistently with water-ice phase transition time. Experiments on resolidification of molten bismuth are also promising.

The process of interest in this study is the solidification of a molten metal subjected to rapid pressurization. Most details about solidification occurring when the liquid‐solid coexistence line is suddenly transversed along the pressure axis remain unknown. We present preliminary results from an ongoing study of this process for both simple models of metals (Cu) and more sophisticated material models (MGPT potentials for Ta). Atomistic (molecular dynamics) simulations are used to extract details such as the time and length scales that govern these processes. Starting with relatively simple potential models, we demonstrate how molecular dynamics can be used to study solidification. Local and global order parameters that aid in characterizing the phase have been identified, and the dependence of the solidification time on the phase space distance between the final (P,T) state and the coexistence line has been characterized.

The use of ultrafast laser pulses to generate very high brightness, ultrashort (10 -14 to 10 -12 s) pulses of x-rays is a topic of great interest to the x-ray user community. In principle, femtosecond-scale pump-probe experiments can be used to temporally resolve structural dynamics of materials on the time scale of atomic motion. However, further development of this field is severely hindered by the absence of a suitably intense x-ray source that would drive the development of improved experimental techniques and establish a broader range of applicability. We report on a project at the Lawrence Livermore National Laboratory to produce a novel x-ray source and essential experimental techniques that will enable unprecedented dynamic measurements in matter. Based on scattering of a sub-50-fs, multi-terawatt, multi-beam laser from a co-synchronous and highly focused relativistic electron bunch, PLEIADES (Picosecond Laser Electron Interaction for Dynamic Evaluation of Structures) will produce tunable, ultrafast, hard x-ray (10- 200 keV) probes that greatly exceed existing 3rd generation synchrotron sources in speed (100 fs - 1 ps), peak brightness (10 20 ph/mm 2 s mrad 2 0.1% BW, and >10 9 ph/pulse), and simplicity (100-fold smaller). Such bright, ultrafast high energy x-rays will enable pump-probe experiments using radiography, dynamic diffraction, and spectroscopy to address the equation of state and dynamics of phase transitions and structure in laser heated and compressed heavy dense metals of interest for materials science.

Molecular dynamics simulations are presented for the stopping of a charged ion in an electron gas. Full Coulomb collisional processes are included, and the resulting particle wake and energy deposition profiles are examined. The present study is extensible to multicomponent plasmas, for evaluating parameters like dE/dx, straggling, blooming, and energy splits.

We report the results of molecular-dynamics simulations on oriented fcc thin films. For thin films of (001) orientation we found that independent of the potential, the intrinsic structural instability of these films governed their elastic properties. All of the in-plane elastic constants increase with decreasing film thickness for thin (111) films modeled with use of embedded-atom-method potentials, whereas (111) films modeled with use of a Lennard-Jones potential gave entirely opposite results.

We have recently developed a novel computational method for molecular dynamics simulations of metal oxide ceramics. This approach explicitly includes variable charge transfer between anions and cations. This method has been used to model the structural properties of bulk and surface alumina and aluminum systems including tensile failure of the bulk systems, as well as to model the rupture under tensile stress of an interface between a (0001) face of a-alumina and a (111) face of aluminum. We have applied this method to perform atomic-scale simulations of nanoindentation of ceramic and model rigid tips onto metal and ceramic substrates.

Lawrence Livermore National Laboratory (LLNL) has a long history of working with IBM on Blue Gene® supercomputers. Beginning in November 2001 with the joint announcement of a partnership to expand the Blue Gene research project (including Blue Gene®/L and Blue Gene®/P), the collaboration extends to this day with LLNL planning for the installation of a 96-rack Blue Gene®/Q (called Sequoia) supercomputer. As with previous machines, we envision Blue Gene/Q will be used for a wide array of applications at LLNL, ranging from meeting programmatic requirements for certification to increasing our understanding of basic physical processes. We briefly describe a representative sample of mature codes that span this application space and scale well on Blue Gene hardware. Finally, we describe advances in multi-scale whole-organ modeling of the human heart as an example of breakthrough science that will be enabled with the Blue Gene/Q architecture.

We present a detailed model describing the effects of surface stress on the equilibrium spacing and biaxial modulus of thin metal films. The model predicts that very thin films will equilibrate to a spacing in the plane substantially smaller than the bulk spacing for the material, and that this biaxial strain will vanish as the reciprocal of the film thickness. The model predicts enhancements in the biaxial modulus of thin metal films which also scale with the reciprocal of the film thickness. The magnitude of both the strain and the resulting change in biaxial modulus are proportional to the magnitude of the surface stress. We verified the predictions of the surface-stress model by performing molecular-dynamics computer simulations of thin metal films using an analytic form of the embedded-atom-method potential. The model was found to predict accurately the equilibrium properties of thin metal films.

We discuss the development of interaction potentials which explicitly allow for charge transfer in metallic oxides. The charge transfer is calculated self-consistently using a charge equilibration approach, which allows the amount of charge transferred to respond to the electrostatic environment. We model the metal-metal, metal-oxygen, and oxygen-oxygen interactions with Rydberg function pair potentials. By fitting the Rydberg potential parameters to the elastic and structural constants of the material, we arrive at an efficient model for the simulation of metallic oxides. We demonstrate the applicability of the model by describing some preliminary results on the rutile phase of titanium dioxide.

Hot dense radiative (HDR) plasmas common to inertial confinement fusion (ICF) and stellar interiors have high temperature (a few hundred eV to tens of keV), high density (tens to hundreds of g/cc) and high pressure (hundreds of Megabars to thousands of Gigabars). Typically, such plasmas undergo collisional, radiative, atomic and possibly thermonuclear processes. In order to describe HDR plasmas, computational physicists in ICF and astrophysics use atomic-scale microphysical models implemented in various simulation codes. Experimental validations of the models used for describing HDR plasmas are difficult to perform. Direct numerical simulation (DNS) of the many-body interactions of plasmas is a promising approach to model validation, but previous work either relies on the collisionless approximation or ignores radiation. We present a first attempt at a new numerical simulation technique to address a currently unsolved problem: the extension of molecular dynamics to collisional plasmas including emission and absorption of radiation. The new technique passes a key test: it relaxes to a blackbody spectrum for a plasma in local thermodynamic equilibrium. This new tool also provides a method for assessing the accuracy of energy and momentum exchange models in hot dense plasmas. As an example, we simulate the evolution of non-equilibrium electron, ion and radiation temperatures for a hydrogen plasma using the new molecular dynamics simulation capability.

We report on the results of molecular dynamics simulations of thin unsupported fcc films ranging in thickness from 20 layers to a monolayer. The films were oriented with either (001) or (111) free surface normals. The atomic interactions were modelled using a standard Lennard-Jones potential and a short range analytic form of the embedded atom potential. The elastic moduli of the films were determined by measuring their response to very low levels of applied stress.We find that the embedded atom and Lennard-Jones results are in relative agreement for (001) films and qualitative disagreement for (111) oriented films. We relate these differences to the nature of the interatomic potential and the thermodynamic instability of the (001) surface.