  Recent advances in the numerical representation of materials opened the way
for successful machine learning of grain boundary (GB) energies and the
classification of GB mobility and shear coupling. Two representations were
needed for these machine learning applications: 1) the ASR representation,
based on averaged local environment descriptors; and 2) the LER descriptor,
based on fractions of globally unique local environments within the entire GB
system. We present a detailed tutorial on how to construct these two
representations to learn energy, mobility and shear coupling. Additionally, we
catalog some of the null results encountered along the way.
