“Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology

Zacharov, I.; Arslanov, Rinat; Gunin, Maksim; Stefonishin, Daniil; Bykov, A. S.; Павлов, С. В.; Panarin, Oleg; Maliutin, Anton; Rykovanov, S. G.; Fedorov, Maxim V.

doi:10.1515/eng-2019-0059

Cited by 203 publications

(105 citation statements)

References 44 publications

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“…Numerical Methods. The large calculations were performed on Skotlech's Zhores computer [28]. The programs were written in Python with Intel optimised libraries.…”

Section: Qaoamentioning

confidence: 99%

Reachability Deficits in Quantum Approximate Optimization

et al. 2020

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The quantum approximate optimization algorithm (QAOA) has rapidly become a cornerstone of contemporary quantum algorithm development. Despite a growing range of applications, only a few results have been developed towards understanding the algorithms ultimate limitations. Here we report that QAOA exhibits a strong dependence on a problem instances constraint to variable ratio-this problem density places a limiting restriction on the algorithms capacity to minimize a corresponding objective function (and hence solve optimization problems). Such reachability deficits persist even in the absence of barren plateaus [1] and are outside of the recently reported level-1 QAOA limitations [2]. Building on general numerical experiments, we compare the presence of reachability deficits with analytic solutions of the variational model of Grover's search algorithm. Comparing QAOA's performance between random 3-SAT (NP-hard) and 2-SAT (efficiently solved) instances, reachability deficits increased with problem density. *

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“…Numerical Methods. The large calculations were performed on Skotlech's Zhores computer [28]. The programs were written in Python with Intel optimised libraries.…”

Section: Qaoamentioning

confidence: 99%

Reachability Deficits in Quantum Approximate Optimization

et al. 2020

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“…The simulations were performed at a computational cluster "Zhores" [26] using 20 nodes. Every node uses two Intel Xeon 6136 Gold CPUs (24 cores, 3 GHz each).…”

Section: Numerical Resultsmentioning

confidence: 99%

Distinct Element Simulation of Mechanical Properties of Hypothetical CNT Nanofabrics

Ostanin

2019

JSFI

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A universal framework for modeling composites and fabrics of micro-and nanofibers, such as carbon nanotubes, carbon fibers and amyloid fibrils, is presented. Within this framework, fibers are represented with chains of rigid bodies, linked with elastic bonds. Elasticity of the bonds utilizes recently developed enhanced vector model formalism. The type of interactions between fibers is determined by their nature and physical length scale of the simulation. The dynamics of fibers is computed using the modification of rigid particle dynamics module of the waLBerla multiphysics framework. Our modeling system demonstrates exceptionally high parallel performance combined with the physical accuracy of the modeling. The efficiency of our technique is demonstrated with an illustrative mechanical test on a hypothetical carbon nanotube textile. In this example, the elasticity of the fibers represents the coarse-grained covalent bond within CNT surface, whereas interfiber interactions represent coarse-grained van der Waals forces between cylindrical segments of nanotubes. Numerical simulation demonstrates stability and extremal strength of a hypothetical carbon nanotube fabric.

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“…The embedded atom method is implemented in the software package LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator by Sandia National Laboratories, Livermore, CA, USA) [36,37]. The calculations were performed on the Lomonosov-2 supercomputer [38,39] (Lomonosov Moscow State University, Russia) and the supercomputer Zhores (CDISE, Skoltech, Russia) [40].…”

Section: Potentialsmentioning

confidence: 99%

Effect of Cold-Sintering Parameters on Structure, Density, and Topology of Fe–Cu Nanocomposites

et al. 2020

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The design of advanced nanostructured materials with predetermined physical properties requires knowledge of the relationship between these properties and the internal structure of the material at the nanoscale, as well as the dependence of the internal structure on the production (synthesis) parameters. This work is the first report of computer-aided analysis of high pressure consolidation (cold sintering) of bimetallic nanoparticles of two immiscible (Fe and Cu) metals using the embedded atom method (EAM). A detailed study of the effect of cold sintering parameters on the internal structure and properties of bulk Fe–Cu nanocomposites was conducted within the limitations of the numerical model. The variation of estimated density and bulk porosity as a function of Fe-to-Cu ratio and consolidation pressure was found in good agreement with the experimental data. For the first time, topological analysis using Minkowski functionals was applied to characterize the internal structure of a bimetallic nanocomposite. The dependence of topological invariants on input processing parameters was described for various components and structural phases. The model presented allows formalizing the relationship between the internal structure and properties of the studied nanocomposites. Based on the obtained topological invariants and Hadwiger’s theorem we propose a new tool for computer-aided design of bimetallic Fe–Cu nanocomposites.

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“Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology

Cited by 203 publications

References 44 publications

Reachability Deficits in Quantum Approximate Optimization

Reachability Deficits in Quantum Approximate Optimization

Distinct Element Simulation of Mechanical Properties of Hypothetical CNT Nanofabrics

Effect of Cold-Sintering Parameters on Structure, Density, and Topology of Fe–Cu Nanocomposites

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