Deconvoluting thermomechanical effects in X-ray diffraction data using machine learning

Bayesian machine learning, trained by physics-based simulations, is used to separate the effects of temperature and mechanical loading within in situ diffraction data.

Materials Science Physics Computer Science Medicine

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References (44)

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TECHNIQUE FOR PROCESSING X-RAY DIFFRACTION DATAUS 10,878,615 · RIGAKU CORPORATION · 2020 X-RAY LASER MICROSCOPY SAMPLE ANALYSIS SYSTEM AND METHODUS 9,859,029 · RISING STAR PATHWAY, A CALIFORNIA CORPORATION · 2018 X-Ray Diffraction Imaging System Using Debye Ring EnvelopesUS 9,921,173 · THE NOTTINGHAM TRENT UNIVERSITY · 2018 PREDICTING ETCH CHARACTERISTICS IN THERMAL ETCHING AND ATOMIC LAYER ETCHINGUS 11,520,953 · LAM RESEARCH CORPORATION · 2022 SYSTEM AND METHOD FOR DECONVOLUTING THE EFFECT OF TOPOGRAPHY ON SCANNING PROBE MICROSCOPY MEASUREMENTSUS 7,366,704 · WATERS INVESTMENT LIMITED · 2008

Source of record

arXiv