Modeling and Optimizing Laser-Induced Graphene

TL;DR

This paper provides datasets and initial modeling approaches for optimizing laser-induced graphene production, addressing key challenges in scaling up this promising manufacturing process.

Contribution

It introduces novel datasets for machine learning applications in laser-induced graphene production and proposes modeling and optimization challenges for future research.

Findings

Initial models demonstrate potential for process understanding

Datasets enable transfer learning across materials

Code facilitates reproducibility and further exploration

Abstract

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.