Machine learning-accelerated small-angle X-ray scattering analysis of disordered two- and three-phase materials
نویسندگان
چکیده
Small-angle X-ray scattering (SAXS) is a useful technique for nanoscale structural characterization of materials. In SAXS, and spatial information indirectly obtained from the intensity in spectral domain, known as reciprocal space. Therefore, characterizing structure requires solving inverse problem finding plausible model that corresponds to measured intensity. Both choice computational workload parameter estimation are bottlenecks this process. work, we develop framework analysis SAXS data disordered The materials modeled using Gaussian Random Fields (GRFs). We study case two phases, pore solid, three where third phase added at interface between other phases. Further, very fast GPU-accelerated, Fourier transform-based numerical methods both generation simulation. demonstrate length scales volume fractions can be predicted with good accuracy our machine learning-based framework. prediction executes virtually instantaneously hence burden conventional fitting avoided.
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ژورنال
عنوان ژورنال: Frontiers in Materials
سال: 2022
ISSN: ['2296-8016']
DOI: https://doi.org/10.3389/fmats.2022.956839