Modeling and Optimizing Laser-Induced Graphene
This addresses the problem of scalable graphene manufacturing for materials science and electronics, but it is incremental as it focuses on dataset creation rather than novel methods.
The paper tackles the challenge of scaling graphene production by providing datasets for modeling, transferring, and optimizing laser-induced graphene manufacturing, with illustrative results and code as a starting point for users.
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.