Hierarchical optimization for neutron scattering problems

Journal Article · Mon Mar 14 00:00:00 EDT 2016 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2016.03.017· OSTI ID:1326532

Bao, Feng ^[1]; Archibald, Rick ^[1]; Bansal, Dipanshu ^[1]; Delaire, Olivier ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

In this study, we present a scalable optimization method for neutron scattering problems that determines confidence regions of simulation parameters in lattice dynamics models used to fit neutron scattering data for crystalline solids. The method uses physics-based hierarchical dimension reduction in both the computational simulation domain and the parameter space. We demonstrate for silicon that after a few iterations the method converges to parameters values (interatomic force-constants) computed with density functional theory simulations.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1326532
• Alternate ID(s): OSTI ID: 1325282
• Journal Information: Journal of Computational Physics, Vol. 315, Issue C; ISSN 0021-9991
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
36 MATERIALS SCIENCE
neutron scattering
model reduction
global optimization
stochastic
confidence distribution