Controlling the Properties of Laser-Induced Graphene by Machine Learning

该项目的目标是使用机器学习（ML）改进激光诱导石墨烯（LIG）的制造。LIG是最近发现的材料，由于其简单的制造和良好的性能而受到了很多关注。LIG可以通过将聚合物（例如柔性塑料箔或3D打印物体）暴露于扫描激光束来创建。无论聚合物在哪里暴露于激光，它都转化为LIG。不需要额外的步骤，如化学合成，石墨烯沉积或光刻。这使得该过程非常简单，快速和低成本。所得LIG是导电的，并且具有多孔微结构，其性质可以通过改变激光参数或聚合物前体的化学性质来调节。为了实现特定应用的最佳LIG特性，由于复杂的基础工艺，需要进行大量的实验或复杂的模拟。这限制了LIG器件的性能以及LIG适应新应用的容易程度。在这里，我们建议训练一个ML模型，以根据需要自动优化LIG的属性。ML的一个主要优势是能够映射输入和输出之间的复杂关系，即使使用传统的分析模型很难做到这一点。我们将使用实验室的实验结果来训练ML模型。该项目的实验部分和机器学习部分之间将不断进行沟通，以确保新收集的数据改进需要进一步训练的模型区域。该项目是加拿大LIG专家和美国ML专家研究人员之间的国际合作。该项目将以多种方式使加拿大受益。LIG可用于各种设备，从物理和生物医学传感器到超级电容器和储能电池，该项目将有利于加拿大此类产品的制造商。更广泛地说，ML对于优化先进制造工艺变得越来越重要，该项目将在技术成果和学生培训方面增加加拿大在这一领域的能力。

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.