Controlling the Properties of Laser-Induced Graphene by Machine Learning

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The goal of this project is to improve the fabrication of laser-induced graphene (LIG) using machine learning (ML). LIG is a recently discovered material that has received a lot of attention due to its simple fabrication and favorable properties. LIG can be created by exposing a polymer, for example a flexible plastic foil or a 3D printed object, to a scanned laser beam. Wherever the polymer is exposed to the laser, it is converted to LIG. No additional steps such as chemical synthesis, deposition of graphene, or photolithography are needed. This makes the process very simple, fast, and low-cost. The resulting LIG is electrically conductive and has a porous microstructure with properties that can be tuned by varying the laser parameters or the chemical properties of the polymer precursor. In order to achieve optimal LIG properties for a particular application, extensive experimentation or complex simulations are necessary due to the complex underlying processes. This limits the performance of LIG devices and how easily LIG can be adapted to a new application. Here, we propose to train a ML model to automatically optimize the properties of LIG as desired. A major strength of ML is to be able to map complex relationships between inputs and outputs even if it is difficult to do so using traditional analytical models. We will train the ML model using experimental results in our lab. There will be a constant communication between the experimental and ML parts of the project to ensure that the newly collected data improves regions of the model that require further training. This project is an international collaboration between researchers in Canada who are experts for LIG and researchers in the United States who are experts for ML. This project will benefit Canada in multiple ways. LIG can be employed in a variety of devices ranging from physical and biomedical sensors to supercapacitors and batteries for energy storage and this project will be beneficial for Canadian manufacturers of such products. More generally, ML is becoming increasingly important to optimize advanced manufacturing processes and this project will add to Canada's capacity in this area both in terms of technical results and student training.