  A lot of technological advances depend on next-generation materials, such as
graphene, which enables a raft of new applications, for example better
electronics. Manufacturing such materials is often difficult; in particular,
producing graphene at scale is an open problem. We provide a series of datasets
that describe the optimization of the production of laser-induced graphene, an
established manufacturing method that has shown great promise. We pose three
challenges based on the datasets we provide -- modeling the behavior of
laser-induced graphene production with respect to parameters of the production
process, transferring models and knowledge between different precursor
materials, and optimizing the outcome of the transformation over the space of
possible production parameters. We present illustrative results, along with the
code used to generate them, as a starting point for interested users. The data
we provide represents an important real-world application of machine learning;
to the best of our knowledge, no similar datasets are available.
