  Efficient and precise prediction of plasticity by data-driven models relies
on appropriate data preparation and a well-designed model. Here we introduce an
unsupervised machine learning-based data preparation method to maximize the
trainability of crystal orientation evolution data during deformation. For
Taylor model crystal plasticity data, the preconditioning procedure improves
the test score of an artificial neural network from 0.831 to 0.999, while
decreasing the training iterations by an order of magnitude. The efficacy of
the approach was further improved with a recurrent neural network. Electron
backscattered (EBSD) lab measurements of crystal rotation during rolling were
compared with the results of the surrogate model, and despite error introduced
by Taylor model simplifying assumptions, very reasonable agreement between the
surrogate model and experiment was observed. Our method is foundational for
further data-driven studies, enabling the efficient and precise prediction of
texture evolution from experimental and simulated crystal plasticity results.
