A Review of Neutron Scattering Applications to Nuclear Materials

Vogel, Sven C.

doi:10.1155/2013/302408

“…Thus, details of the carbon or oxygen sublattices become vanishingly small, which are the decisive difference between the three crystal structures to be resolved in this work (see [15] for a comparison of simulated neutron and X-ray diffraction patterns for these structures.…”

Section: Introductionmentioning

confidence: 99%

In situ synthesis and characterization of uranium carbide using high temperature neutron diffraction

Reiche

¹

,

Vogel

²

,

Tang

³

2016

Journal of Nuclear Materials

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We investigated the formation of UCx from UO2+x and graphite in situ using neutron diffraction at high temperatures with particular focus on resolving the conflicting reports on the crystal structure of non-quenchable cubic UC2. The agents were UO2 nanopowder, which closely imitates nano grains observed in spent reactor fuels, and graphite powder. In situ neutron diffraction revealed the onset of the UO2 + 2C  UC + CO2 reaction at 1440˚C, with its completion at 1500˚C. Upon further heating, carbon diffuses into the uranium carbide forming C2 groups at the octahedral sites. This resulting high temperature cubic UC2 phase is similar to the NaCl-type structure as proposed by Bowman et al. Our novel experimental data provide insights into the mechanism and kinetics of formation of UC as well as characteristics of the high

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“…Scattering techniques are used extensively for material characterization and structure determination [76] . Neutron scattering in particular has applications in the field of condensed matter physics, [77,78] nuclear physics and nuclear materials research, [79] biology [80] , crystallography, and catalysis. [78] For this work, we demonstrate that HTF can be used to approximate the neutron scattering profile of a simulation box at every timestep.…”

Section: Calculating Scattering Profiles (Arbitrary Collective Variabmentioning

confidence: 99%

A GPU-Accelerated Machine Learning Framework for Molecular Simulation: Hoomd-Blue with TensorFlow

Barrett¹,

Chakraborty²,

Gandhi³

et al. 2019

Preprint

0

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We have designed and implemented software that enables integration of a scalable GPU-accelerated molecular mechanics engine, Hoomd-blue, with the machine learning (ML) TensorFlow package. TensorFlow is a GPU accelerated, scalable, graph-based tensor computation model building package that has been the implementation of many recent innovations in deep learning and other ML tasks. Tensor computation graphs allow for designation of robust, flexible, and easily replicated computational models for a variety of tasks. Our plugin leverages the generality and speed of computational tensor graphs in TensorFlow to enable four previously challenging tasks in molecular dynamics: (1) the calculation of arbitrary force-fields including neural-network-based, stochastic, and/or automatically-generated force-fields which are differentiated from potential functions; (2) the efficient computation of arbitrary collective variables; (3) the biasing of simulations via automatic differentiation of collective variables and consequently the implementation of many free energy biasing methods; (4) ML on any of the above tasks, including coarse grain force fields, on-the-fly learned biases, and collective variable calculations. The TensorFlow models are constructed in Python and can be visualized or debugged using the rich set of tools implemented in the TensorFlow package. In this article, we present examples of the four major tasks this method can accomplish, benchmark data, and describe the architecture of our implementation. This method should lead to both the design of new models in computational chemistry research and reproducible model specification without requiring recompiling or writing low-level code. <br>

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“…Scattering techniques are used extensively for material characterization and structure determination. [79] Neutron scattering in particular has applications in the field of condensed matter physics, [80,81] nuclear physics and nuclear materials research, [82] biology, [83] crystallography, and catalysis. [81] For this work, we demonstrate that HTF can be used to approximate the neutron scattering profile of a simulation box at every time step, on-the-fly.…”

Section: Calculating Scattering Profiles (Arbitrary Collective Variabmentioning

confidence: 99%

A GPU-Accelerated Machine Learning Framework for Molecular Simulation: Hoomd-Blue with TensorFlow

Barrett¹,

Chakraborty²,

Gandhi³

et al. 2019

Preprint

0

View full text Add to dashboard Cite

We have designed and implemented software that enables integration of a scalable GPU-accelerated molecular mechanics engine, Hoomd-blue, with the machine learning (ML) TensorFlow package. TensorFlow is a GPU accelerated, scalable, graph-based tensor computation model building package that has been the implementation of many recent innovations in deep learning and other ML tasks. Tensor computation graphs allow for designation of robust, flexible, and easily replicated computational models for a variety of tasks. Our plugin leverages the generality and speed of computational tensor graphs in TensorFlow to enable four previously challenging tasks in molecular dynamics: (1) the calculation of arbitrary force-fields including neural-network-based, stochastic, and/or automatically-generated force-fields which are differentiated from potential functions; (2) the efficient computation of arbitrary collective variables; (3) the biasing of simulations via automatic differentiation of collective variables and consequently the implementation of many free energy biasing methods; (4) ML on any of the above tasks, including coarse grain force fields, on-the-fly learned biases, and collective variable calculations. The TensorFlow models are constructed in Python and can be visualized or debugged using the rich set of tools implemented in the TensorFlow package. In this article, we present examples of the four major tasks this method can accomplish, benchmark data, and describe the architecture of our implementation. This method should lead to both the design of new models in computational chemistry research and reproducible model specification without requiring recompiling or writing low-level code. <br>

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A Review of Neutron Scattering Applications to Nuclear Materials

Cited by 32 publications

References 188 publications

In situ synthesis and characterization of uranium carbide using high temperature neutron diffraction

In situ synthesis and characterization of uranium carbide using high temperature neutron diffraction

A GPU-Accelerated Machine Learning Framework for Molecular Simulation: Hoomd-Blue with TensorFlow

A GPU-Accelerated Machine Learning Framework for Molecular Simulation: Hoomd-Blue with TensorFlow

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