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
Section A FOUNDATIONS AND ADVANCES

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Figure 9
Accuracy predicting reserved testing data of trained surrogate models to determine the minimum of (a) $[\varepsilon^{\rm T}]$ , (b) $[\varepsilon^{\rm E}_{xx}]$ , (c) $[\varepsilon^{\rm E}_{zz}]$ and (d) $[\varepsilon^{\rm E}_{xz}]$ within a diffraction volume from input diffraction spectra. The dashed blue line denotes one-to-one correlation, and the black line is a linear fit.