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

Lim, R.E.; Shang, S.-L.; Chuang, C.; Phan, T.Q.; Liu, Z.-K.; Pagan, D.C.

doi:10.1107/S2053273325000403

Acta Crystallographica Section A
Acta Crystallographica
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Figure 19
The evolving (a) mean $[\varepsilon^{\rm E}_{zz{\hbox{-}}{\rm Mean}}]$ , (b) maximum $[\varepsilon^{\rm E}_{zz{\hbox{-}}{\rm Max.}}]$ , (c) minimum $[\varepsilon^{\rm E}_{zz{\hbox{-}}{\rm Min.}}]$ and (d) standard deviation $[\varepsilon^{\rm E}_{zz{\hbox{-}}{\rm STD}}]$ of the distribution of elastic strain within the duplicate experimental X-ray diffraction volume with respect to time t extracted using trained GPR surrogate models. The red error bars correspond to the square root of the variance (standard deviation) of the GPR surrogate model predictions.

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ISSN: 2053-2733

Volume 81| Part 2| March 2025|

https://doi.org/10.1107/S2053273325000403

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