2D-diffractogram analysis: Kinematic-diffraction simulator for neural-network training-data generation

doi:10.1016/j.commatsci.2025.113777

2D-diffractogram analysis: Kinematic-diffraction simulator for neural-network training-data generation

2025-04-01

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DOI：10.1016/j.commatsci.2025.113777

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To exploit the information contained in 2D X-ray diffractograms fully, quantitatively, automatically, and with high throughput, e.g. for analyzing video sequences from in-situ experiments, we can train deep- learning NNs (neural networks) with simulated diffractograms. Realistic models of materials microstructures require ground truthtraining datasets of high cardinality. To produce these, we developed a kinematic- diffraction simulator,implemented in the Wolfram Language and executed within a high-performance computing environment. The simulator can rapidly generate Fraunhofer diffractograms for diverse crystal- and microstructure models over a significant multi-dimensional space of parameters. We conclude that simulated diffractograms can enable suitable training of deep-learning NNs - in spite of not including some real-worldfeatures that occur in experimental diffractograms - and that high-performance computing achieves training data generation rates that support modeling of microstructures with a realistically large number of parameters.

Keywords:

X-ray diffractometry

Diffractogram analysis

Diffraction simulator

Machine learning

Neural networks

2D-diffractogram analysis: Kinematic-diffraction simulator for neural-network training-data generation

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