Simulating materials failure by using up to one billion atoms and the world's fastest computer: Brittle fracture

Abraham, Farid F.; Walkup, R. E.; Gao, Huajian; Duchaineau, Mark A.; Rubia, T. Dı́az de la; Seager, Mark

doi:10.1073/pnas.062012699

Cited by 128 publications

(81 citation statements)

References 18 publications

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“…[9][10][11][12][13][14][15][16][17] It has been known for long that pair potentials are capable to model rare gas solids, but they have several deficits in modelling metallic materials. Thus they allow to prescribe only two -rather than three -elastic constants for solids, they model an outwards -rather than an inwards -surface relaxation, etc.…”

Section: Introductionmentioning

confidence: 99%

Pair vs many-body potentials: Influence on elastic and plastic behavior in nanoindentation of fcc metals

Ziegenhain

Hartmaier

Urbassek

2009

Journal of the Mechanics and Physics of Solids

117

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Molecular-dynamics simulation can give atomistic information on the processes occurring in nanoindentation experiments. In particular, the nucleation of dislocation loops, their growth, interaction and motion can be studied. We investigate how realistic the interatomic potentials underlying the simulations have to be in order to describe these complex processes. Specifically we investigate nanoindentation into a Cu single crystal. We compare simulations based on a realistic many-body interaction potential of the embedded-atom-method type with two simple pair potentials, a Lennard-Jones and a Morse potential. We find that qualitatively many aspects of nanoindentation are fairly well reproduced by the simple pair potentials: elastic regime, critical stress and indentation depth for yielding, dependence on the crystal orientation, and even the level of the hardness. The quantitative deficits of the pair potential predictions can be traced back (i) to the fact that the pair potentials are unable in principle to model the elastic anisotropy of cubic crystals; (ii) as the major drawback of pair potentials we identify the gross underestimation of the stable stacking fault energy. As a consequence these potentials predict the formation of too large dislocation loops, the too rapid expansion of partials, too little cross slip and in consequence a severe overestimation of work hardening.

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Section: Introductionmentioning

confidence: 99%

Pair vs many-body potentials: Influence on elastic and plastic behavior in nanoindentation of fcc metals

Ziegenhain

Hartmaier

Urbassek

2009

Journal of the Mechanics and Physics of Solids

117

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“…A promising approach is hierarchical simulation (Broughton et al 1999;Nakano et al 2001;Ogata et al 2001), in which atomistic molecular dynamics (MD) simulations Abraham et al 2002;Kadau et al 2002;Nakano et al 2002) of varying accuracy and computational costs (from classical non-reactive MD to chemically reactive MD based on semi-classical approaches) explore a wide solution space to discover new mechanisms, in which highly accurate quantum mechanical (QM) simulations (Car and Parrinello 1985;Kendall et al 2000;Truhlar and McKoy 2000;Gygi et al 2005;Ikegami et al 2005) are embedded to validate the discovered mechanisms and quantify the uncertainty of the solution.…”

Section: Introductionmentioning

confidence: 99%

De Novo Ultrascale Atomistic Simulations On High-End Parallel Supercomputers

Nakano

Kalia

Nomura

et al. 2008

The International Journal of High Performance Computing Applica

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We present a de novo hierarchical simulation framework for first-principles based predictive simulations of materials and their validation on high-end parallel supercomputers and geographically distributed clusters. In this framework, highend chemically reactive and non-reactive molecular dynamics (MD) simulations explore a wide solution space to discover microscopic mechanisms that govern macroscopic material properties, into which highly accurate quantum mechanical (QM) simulations are embedded to validate the discovered mechanisms and quantify the uncertainty of the solution. The framework includes an embedded divide-andconquer (EDC) algorithmic framework for the design of linear-scaling simulation algorithms with minimal bandwidth complexity and tight error control. The EDC framework also enables adaptive hierarchical simulation with automated model transitioning assisted by graph-based event tracking. A tunable hierarchical cellular decomposition parallelization framework then maps the O(N) EDC algorithms onto petaflops computers, while achieving performance tunability through a hierarchy of parameterized cell data/ computation structures, as well as its implementation using hybrid grid remote procedure call + message passing + threads programming. High-end computing platforms such as IBM BlueGene/L, SGI Altix 3000 and the NSF TeraGrid provide an excellent test grounds for the framework. On these platforms, we have achieved unprecedented scales of quantum-mechanically accurate and well validated, chemically reactive atomistic simulations-1.06 billion-atom fast reactive force-field MD and 11.8 million-atom (1.04 trillion grid points) quantum-mechanical MD in the framework of the EDC density functional theory on adaptive multigridsin addition to 134 billion-atom non-reactive space-time multiresolution MD, with the parallel efficiency as high as 0.998 on 65,536 dual-processor BlueGene/L nodes. We have also achieved an automated execution of hierarchical QM/MD simulation on a grid consisting of 6 supercomputer centers in the US and Japan (in total of 150,000 processor hours), in which the number of processors change dynamically on demand and resources are allocated and migrated dynamically in response to faults. Furthermore, performance portability has been demonstrated on a wide range of platforms such as BlueGene/L, Altix 3000, and AMD Opteron-based Linux clusters.

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“…However, many existing MD algorithms have limitations either on length or on time scales due to the lack of enough compute power. Even the most powerful high performance computing (HPC) system is still not powerful enough to perform a complete MD simulation [2]. Therefore, an innovative computational methodology for MD simulations is urgently needed.…”

Section: Introductionmentioning

confidence: 99%

A Grid-Based Bridging Domain Multiple-Scale Method for Computational Nanotechnology

Wang

Xiao

2005

Lecture Notes in Computer Science

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Abstract. This paper presents an application-level Grid middleware framework to support a bridging domain multi-scale method fornumerical modeling and simulation in nanotechnology. The framework considers a multiple-length-scale model by designing specific domain-decomposition and communication algorithms based on Grid computing. The framework is designed to enable researchers to conductlarge-scale computing in computational nanotechnology through the use of Grid resources for exploring microscopic physical properties of materials without losing details of nanoscale physical phenomena.

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Simulating materials failure by using up to one billion atoms and the world's fastest computer: Brittle fracture

Cited by 128 publications

References 18 publications

Pair vs many-body potentials: Influence on elastic and plastic behavior in nanoindentation of fcc metals

Pair vs many-body potentials: Influence on elastic and plastic behavior in nanoindentation of fcc metals

De Novo Ultrascale Atomistic Simulations On High-End Parallel Supercomputers

A Grid-Based Bridging Domain Multiple-Scale Method for Computational Nanotechnology

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