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Evaluation of nano‐ and mesoscale structural features in composite materials through hierarchical decomposition of the radial distribution function
Author(s) -
García-Negrón Valerie,
Oyedele Akinola D.,
Ponce Eduardo,
Rios Orlando,
Harper David P.,
Keffer David J.
Publication year - 2018
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s1600576717016843
Subject(s) - amorphous solid , radial distribution function , neutron diffraction , diffraction , neutron scattering , materials science , statistical physics , distribution function , composite number , decomposition , scattering , pair distribution function , computer science , molecular dynamics , algorithm , physics , crystallography , chemistry , optics , computational chemistry , thermodynamics , organic chemistry , quantum mechanics
Composite materials possessing both crystalline and amorphous domains, when subjected to X‐ray and neutron scattering, generate diffraction patterns that are often difficult to interpret. One approach is to perform atomistic simulations of a proposed structure, from which the analogous diffraction pattern can be obtained for validation. The structure can be iteratively refined until simulation and experiment agree. The practical drawback to this approach is the significant computational resources required for the simulations. In this work, an alternative approach based on a hierarchical decomposition of the radial distribution function is used to generate a physics‐based model allowing rapid interpretation of scattering data. In order to demonstrate the breadth of this approach, it is applied to a series of carbon composites. The model is compared with atomistic simulation results in order to demonstrate that the contributions of the crystalline and amorphous domains, as well as their interfaces, are correctly captured. Because the model is more efficient, additional structural refinement is performed to increase the agreement of the simulation result with the experimental data. The model achieves a reduction in computational effort of six orders of magnitude relative to simulation. The model can be generally extended to other composite materials.