Publications

Davis, Jean-Paul D.; Lane, James M.; Thompson, Aidan P.; Drake, Richard R.; Weirs, Vincent G.; Flicker, Dawn G.; Robbins, Joshua R.; Lemke, Raymond W.; Martin, Matthew; Alexander, Charles S.; Haill, Thomas A.; Ao, Tommy A.; Dolan, Daniel H.

Abstract not provided.

Thompson, Aidan P.; Trott, Christian R.

Within the EXAALT project, the SNAP [1] approach is being used to develop high accuracy potentials for use in large-scale long-time molecular dynamics simulations of materials behavior. In particular, we have developed a new SNAP potential that is suitable for describing the interplay between helium atoms and vacancies in high-temperature tungsten[2]. This model is now being used to study plasma-surface interactions in nuclear fusion reactors for energy production. The high-accuracy of SNAP potentials comes at the price of increased computational cost per atom and increased computational complexity. The increased cost is mitigated by improvements in strong scaling that can be achieved using advanced algorithms [3].

Crozier, Paul C.; Plimpton, Steven J.; Thompson, Aidan P.

Abstract not provided.

Lo, Chi S.; Thompson, Aidan P.

Abstract not provided.

Foiles, Stephen M.; Thompson, Aidan P.; Swiler, Laura P.; Trott, Christian R.

Abstract not provided.

Macromolecules

Budzien, Joanne L.; Lo, Chi S.; Thompson, Aidan P.

Abstract not provided.

Wood, Mitchell A.; Plimpton, Steven J.; Thompson, Aidan P.; Perez, Danny P.; Niklasson, Anders N.

Abstract not provided.

Thompson, Aidan P.

Abstract not provided.

International Journal of High Performance Computing Applications

Levy, Scott; Ferreira, Kurt; Bridges, Patrick G.; Thompson, Aidan P.; Trott, Christian R.

Building the next-generation of extreme-scale distributed systems will require overcoming several challenges related to system resilience. As the number of processors in these systems grow, the failure rate increases proportionally. One of the most common sources of failure in large-scale systems is memory. In this paper, we propose a novel runtime for transparently exploiting memory content similarity to improve system resilience by reducing the rate at which memory errors lead to node failure. We evaluate the viability of this approach by examining memory snapshots collected from eight high-performance computing (HPC) applications and two important HPC operating systems. Based on the characteristics of the similarity uncovered, we conclude that our proposed approach shows promise for addressing system resilience in large-scale systems.

Deng, Jie D.; Erickson, Lindsay C.; Plimpton, Steven J.; Thompson, Aidan P.; Zhou, Xiaowang Z.; Zimmerman, Jonathan A.

Many of the most important and hardest-to-solve problems related to the synthesis, performance, and aging of materials involve diffusion through the material or along surfaces and interfaces. These diffusion processes are driven by motions at the atomic scale, but traditional atomistic simulation methods such as molecular dynamics are limited to very short timescales on the order of the atomic vibration period (less than a picosecond), while macroscale diffusion takes place over timescales many orders of magnitude larger. We have completed an LDRD project with the goal of developing and implementing new simulation tools to overcome this timescale problem. In particular, we have focused on two main classes of methods: accelerated molecular dynamics methods that seek to extend the timescale attainable in atomistic simulations, and so-called 'equation-free' methods that combine a fine scale atomistic description of a system with a slower, coarse scale description in order to project the system forward over long times.

Physical Review B

Ellis, J.A.; Fiedler, L.; Popoola, G.A.; Modine, N.A.; Stephens, John A.; Thompson, Aidan P.; Cangi, A.; Rajamanickam, Sivasankaran R.

We present a numerical modeling workflow based on machine learning which reproduces the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.

Ellis, J.A.; Rajamanickam, Sivasankaran R.; Modine, N.A.; Thompson, Aidan P.; Stephens, John A.; Cangi, Attila -.

Abstract not provided.

Modine, N.A.; Stephens, John A.; Swiler, Laura P.; Thompson, Aidan P.; Vogel, Dayton J.; Cangi, Attila C.; Feilder, Lenz F.; Rajamanickam, Sivasankaran R.

The focus of this project is to accelerate and transform the workflow of multiscale materials modeling by developing an integrated toolchain seamlessly combining DFT, SNAP, LAMMPS, (shown in Figure 1-1) and a machine-learning (ML) model that will more efficiently extract information from a smaller set of first-principles calculations. Our ML model enables us to accelerate first-principles data generation by interpolating existing high fidelity data, and extend the simulation scale by extrapolating high fidelity data (10² atoms) to the mesoscale (10⁴ atoms). It encodes the underlying physics of atomic interactions on the microscopic scale by adapting a variety of ML techniques such as deep neural networks (DNNs), and graph neural networks (GNNs). We developed a new surrogate model for density functional theory using deep neural networks. The developed ML surrogate is demonstrated in a workflow to generate accurate band energies, total energies, and density of the 298K and 933K Aluminum systems. Furthermore, the models can be used to predict the quantities of interest for systems with more number of atoms than the training data set. We have demonstrated that the ML model can be used to compute the quantities of interest for systems with 100,000 Al atoms. When compared with 2000 Al system the new surrogate model is as accurate as DFT, but three orders of magnitude faster. We also explored optimal experimental design techniques to choose the training data and novel Graph Neural Networks to train on smaller data sets. These are promising methods that need to be explored in the future.

Thompson, Aidan P.; Slepoy, Alexander S.; Davidson, George S.; Peters, Michael D.

Abstract not provided.

Wills, Ann E.; Thompson, Aidan P.; Raman, Sumathy R.

We establish an atomistic view of the high- and low-temperature phases of iron/steel as well as some elements of the phase transition between these phases on cooling. In particular we examine the 4 most common orientation relationships between the high temperature austen- ite and low-temperature ferrite phases seen in experiment. With a thorough understanding of these relationships we are prepared to set up various atomistic simulations, using tech- niques such as Density Functional Theory and Molecular Dynamics, to further study the phase transition, in particular, quantities needed for Phase Field Modeling, such as the free energies of bulk phases and the phase transition front propagation velocity.

Ferreira, Kurt; Thompson, Aidan P.; Trott, Christian R.; Levy, Scott L.

Abstract not provided.

Budzien, Joanne L.; Lo, Chi S.; Grest, Gary S.; Thompson, Aidan P.

Abstract not provided.

Shan, Tzu-Ray S.; Thompson, Aidan P.; Yarrington, Cole Y.; Kittell, David E.; Wixom, Ryan R.

Abstract not provided.

Shan, Tzu-Ray S.; Thompson, Aidan P.; Wills, Ann E.

Abstract not provided.

Thompson, Aidan P.; Shan, Tzu-Ray S.; Wixom, Ryan R.; Yarrington, Cole Y.

Abstract not provided.

Thompson, Aidan P.

Abstract not provided.

Thompson, Aidan P.; Schultz, Peter A.; Crozier, Paul C.; Moore, Stan G.; Swiler, Laura P.; Stephens, John A.; Trott, Christian R.; Foiles, Stephen M.; Tucker, Garritt J.

This report summarizes the result of LDRD project 12-0395, titled "Automated Algorithms for Quantum-level Accuracy in Atomistic Simulations." During the course of this LDRD, we have developed an interatomic potential for solids and liquids called Spectral Neighbor Analysis Poten- tial (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The SNAP coef- ficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. Global optimization methods in the DAKOTA software package are used to seek out good choices of hyperparameters that define the overall structure of the SNAP potential. FitSnap.py, a Python-based software pack- age interfacing to both LAMMPS and DAKOTA is used to formulate the linear regression problem, solve it, and analyze the accuracy of the resultant SNAP potential. We describe a SNAP potential for tantalum that accurately reproduces a variety of solid and liquid properties. Most significantly, in contrast to existing tantalum potentials, SNAP correctly predicts the Peierls barrier for screw dislocation motion. We also present results from SNAP potentials generated for indium phosphide (InP) and silica (SiO 2 ). We describe efficient algorithms for calculating SNAP forces and energies in molecular dynamics simulations using massively parallel computers and advanced processor ar- chitectures. Finally, we briefly describe the MSM method for efficient calculation of electrostatic interactions on massively parallel computers.

Thompson, Aidan P.; Schultz, Peter A.; Swiler, Laura P.; Trott, Christian R.; Tucker, Garritt T.; Foiles, Stephen M.

Abstract not provided.

Alexander, Charles S.; Thompson, Aidan P.; Chhabildas, Lalit C.; Furnish, Michael D.; Dolan, Daniel H.; Davis, Jean-Paul D.; Lemke, Raymond W.; Desjarlais, Michael P.; Hertel, Eugene S.

Abstract not provided.

Nuclear Fusion

Cusentino, Mary A.; Wood, M.A.; Thompson, Aidan P.

Erosion of the beryllium first wall material in tokamak reactors has been shown to result in transport and deposition on the tungsten divertor. Experimental studies of beryllium implantation in tungsten indicate that mixed W-Be intermetallic deposits can form, which have lower melting temperatures than tungsten and can trap tritium at higher rates. To better understand the formation and growth rate of these intermetallics, cumulative molecular dynamics (MD) simulations of both high and low energy beryllium deposition in tungsten were performed. In both cases, a W-Be mixed material layer (MML) emerged at the surface within several nanoseconds, either through energetic implantation or a thermally-activated exchange mechanism, respectively. While some ordering of the material into intermetallics occurred, fully ordered structures did not emerge from the deposition simulations. Targeted MD simulations of the MML to further study the rate of Be diffusion and intermetallic growth rates indicate that for both cases, the gradual re-structuring of the material into an ordered intermetallic layer is beyond accessible MD time scales(≼1 μs). However, the rapid formation of the MML within nanoseconds indicates that beryllium deposition can influence other plasma species interactions at the surface and begin to alter the tungsten material properties. Therefore, beryllium deposition on the divertor surface, even in small amounts, is likely to cause significant changes in plasma-surface interactions and will need to be considered in future studies.

Wood, Mitchell A.; Sievers, Charles A.; Thompson, Aidan P.; Lubbers, Nicholas E.; Danny, Perez D.

Abstract not provided.

Thompson, Aidan P.

The purpose of this work is to understand how defects control initiation in energetic materials used in stockpile components; Sequoia gives us the core-count to run very large-scale simulations of up to 10 million atoms and; Using an OpenMP threaded implementation of the ReaxFF package in LAMMPS, we have been able to get good parallel efficiency running on 16k nodes of Sequoia, with 1 hardware thread per core.

Lane, James M.; Thompson, Aidan P.

Abstract not provided.

Lane, James M.; Thompson, Aidan P.; Zimmerman, Jonathan A.

Abstract not provided.

Proposed for publication in Materials Research Society.

Plimpton, Steven J.; Thompson, Aidan P.

Abstract not provided.

Budzien, Joanne L.; Lo, Chi S.; Curro, John G.; Thompson, Aidan P.; Grest, Gary S.

Constitutive models for chemically reacting networks are formulated based on a generalization of the independent network hypothesis. These models account for the coupling between chemical reaction and strain histories, and have been tested by comparison with microscopic molecular dynamics simulations. An essential feature of these models is the introduction of stress transfer functions that describe the interdependence between crosslinks formed and broken at various strains. Efforts are underway to implement these constitutive models into the finite element code Adagio. Preliminary results are shown that illustrate the effects of changing crosslinking and scission rates and history.

Moore, Stan G.; Thompson, Aidan P.; Gayatri, Rahul G.

Abstract not provided.

Tranchida, Julien G.; Thompson, Aidan P.; Plimpton, Steven J.; Thibaudeau, Pascal T.

Abstract not provided.

Tranchida, Julien G.; Thompson, Aidan P.; Plimpton, Steven J.; Thibaudeau, Pascal T.

Abstract not provided.

Tranchida, Julien G.; Thompson, Aidan P.; Plimpton, Steven J.; Pillars, Jamin R.; Monson, Todd M.; Langlois, Eric L.; Cangi, Attila C.; Thibaudeau, Pascal T.

Abstract not provided.

Plimpton, Steven J.; Battaile, Corbett C.; Chandross, M.; Holm, Elizabeth A.; Thompson, Aidan P.; Tikare, Veena T.; Webb, Edmund B.; Zhou, Xiaowang Z.

The kinetic Monte Carlo method and its variants are powerful tools for modeling materials at the mesoscale, meaning at length and time scales in between the atomic and continuum. We have completed a 3 year LDRD project with the goal of developing a parallel kinetic Monte Carlo capability and applying it to materials modeling problems of interest to Sandia. In this report we give an overview of the methods and algorithms developed, and describe our new open-source code called SPPARKS, for Stochastic Parallel PARticle Kinetic Simulator. We also highlight the development of several Monte Carlo models in SPPARKS for specific materials modeling applications, including grain growth, bubble formation, diffusion in nanoporous materials, defect formation in erbium hydrides, and surface growth and evolution.

npj Computational Materials

Nikolov, Svetoslav V.; Wood, Mitchell A.; Cangi, Attila; Maillet, Jean B.; Marinica, Mihai C.; Thompson, Aidan P.; Desjarlais, Michael P.; Tranchida, Julien G.

A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP. Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed. Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations. We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP for α-iron. The combined potential energy surface yields excellent agreement with first-principles magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus, magnetization, and specific heat across the ferromagnetic–paramagnetic phase transition.

Physical Review B

Wood, M.A.; Cusentino, Mary A.; Wirth, B.D.; Thompson, Aidan P.

The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and find accurate parametrizations for potentials using traditional approaches. Machine learning has emerged as an effective alternative approach to develop accurate and robust interatomic potentials. Starting with a very general model form, the potential is learned directly from a database of electronic structure calculations and therefore can be viewed as a multiscale link between quantum and classical atomistic simulations. Risk of inaccurate extrapolation exists outside the narrow range of time and length scales where the two methods can be directly compared. In this work, we use the spectral neighbor analysis potential (SNAP) and show how a fit can be produced with minimal interpolation errors which is also robust in extrapolating beyond training. To demonstrate the method, we have developed a tungsten-beryllium potential suitable for the full range of binary compositions. Subsequently, large-scale molecular dynamics simulations were performed of high energy Be atom implantation onto the (001) surface of solid tungsten. The machine learned W-Be potential generates a population of implantation structures consistent with quantum calculations of defect formation energies. A very shallow (<2nm) average Be implantation depth is predicted which may explain ITER diverter degradation in the presence of beryllium.

Cochrane, Kyle C.; Desjarlais, Michael P.; Lane, James M.; Leung, Kevin L.; Thompson, Aidan P.; Wright, Alan F.

Abstract not provided.

Proposed for publication in Jounal of Physical Chemistry A.

Thompson, Aidan P.; Shan, Tzu-Ray S.

Abstract not provided.

Cusentino, Mary A.; Wood, Mitchell A.; Thompson, Aidan P.

Abstract not provided.

Cusentino, Mary A.; Bobbitt, Nathaniel S.; Wood, Mitchell A.; Thompson, Aidan P.

Abstract not provided.

Cusentino, Mary A.; Wood, Mitchell A.; Thompson, Aidan P.

Abstract not provided.

Cusentino, Mary A.; Wood, Mitchell A.; Thompson, Aidan P.

Abstract not provided.

Thompson, Aidan P.

Abstract not provided.

Journal of Physical Chemistry B

Chandross, M.; Webb, Edmund B.; Grest, Gary S.; Martin, Marcus G.; Thompson, Aidan P.; Roth, M.W.

We present the results of Molecular Dynamics and Monte Carlo simulations of n-butane and isobutane in silicalite. We begin with a comparison of the bulk adsorption and diffusion properties for two different parameterizations of the interaction potential between the hydrocarbon species, both of which have been shown to reproduce experimental gas-liquid coexistence curves. We examine diffusion as a function of the loading of the zeolite, as well as the temperature dependence of the diffusion constant at loading and for infinite dilution. Both force fields give accurate descriptions of bulk properties. We continue with simulations in which interfaces are formed between single component gases and the zeolite. After reaching equilibrium, we examine the dynamics of exchange between the bulk gas and the zeolite. In particular, we examine the average time spent in the adsorption layer by molecules as they enter the zeolite from the gas in an attempt to probe the microscopic origins of the surface barrier. The microscopic barrier is found to be insignificant for experimental systems. Finally, we calculate the permeability of the zeolite for n-butane and isobutane as a function of pressure. Our results underestimate the experimental results by an order of magnitude, indicating a strong effect from the surface barrier in these simulations. Our simulations are performed for a number of different gas temperatures and pressures, covering a wide range of state points.

Journal of Chemical Physics

Martin, Marcus G.; Thompson, Aidan P.; Nenoff, T.M.

Abstract not provided.

Desjarlais, Michael P.; Thompson, Aidan P.; Brennecka, Geoffrey L.; Marinella, Matthew J.

Density-functional theory calculations, ab-initio molecular dynamics, and the Kubo-Greenwood formula are applied to predict electrical conductivity in Ta2Ox (0 x 5) as a function of composition, phase, and temperature, where additional focus is given to various oxidation states of the O monovacancy (VOn; n=0,1+,2+). Our calculations of DC conductivity at 300K agree well with experimental measurements taken on Ta2Ox thin films and bulk Ta2O5 powder-sintered pellets, although simulation accuracy can be improved for the most insulating, stoichiometric compositions. Our conductivity calculations and further interrogation of the O-deficient Ta2O5 electronic structure provide further theoretical basis to substantiate VO0 as a donor dopant in Ta2O5 and other metal oxides. Furthermore, this dopant-like behavior appears specific to neutral VO cases in both Ta2O5 and TiO2 and was not observed in other oxidation states. This suggests that reduction and oxidation reactions may effectively act as donor activation and deactivation mechanisms, respectively, for VO0 in transition metal oxides.

Physical Review B - Condensed Matter and Materials Physics

Edwards, Arthur H.; Pineda, Andrew C.; Schultz, Peter A.; Martin, Marcus G.; Thompson, Aidan P.; Hjalmarson, Harold P.; Umrigar, Cyrus J.

Germanium telluride undergoes rapid transition between polycrystalline and amorphous states under either optical or electrical excitation. While the crystalline phases are predicted to be semiconductors, polycrystalline germanium telluride always exhibits p -type metallic conductivity. We present a study of the electronic structure and formation energies of the vacancy and antisite defects in both known crystalline phases. We show that these intrinsic defects determine the nature of free-carrier transport in crystalline germanium telluride. Germanium vacancies require roughly one-third the energy of the other three defects to form, making this by far the most favorable intrinsic defect. While the tellurium antisite and vacancy induce gap states, the germanium counterparts do not. A simple counting argument, reinforced by integration over the density of states, predicts that the germanium vacancy leads to empty states at the top of the valence band, thus giving a complete explanation of the observed p -type metallic conduction.

Thompson, Aidan P.; Aidun, John B.; Schmitt, Robert G.

We implemented two numerical simulation capabilities essential to reliably predicting the effect of non-ideal explosives (NXs). To begin to be able to treat the multiple, competing, multi-step reaction paths and slower kinetics of NXs, Sandia's CTH shock physics code was extended to include the TIGER thermochemical equilibrium solver as an in-line routine. To facilitate efficient exploration of reaction pathways that need to be identified for the CTH simulations, we implemented in Sandia's LAMMPS molecular dynamics code the MSST method, which is a reactive molecular dynamics technique for simulating steady shock wave response. Our preliminary demonstrations of these two capabilities serve several purposes: (i) they demonstrate proof-of-principle for our approach; (ii) they provide illustration of the applicability of the new functionality; and (iii) they begin to characterize the use of the new functionality and identify where improvements will be needed for the ultimate capability to meet national security needs. Next steps are discussed.