Author Description

<p>This primer explains at a fundamental level how machine learning models are created, trained, and evaluated while focusing specifically on applications in polymer informatics. The authors introduce techniques suited to polymer data, providing a foundational understanding that the reader can use as a launchpad for more advanced methods. Additionally, to serve as an interactive aid for learning within this primer, the authors have also provided tutorials at the end of each chapter. With this additional tool, the reader will be well-equipped to begin implementing these machine learning models independently and contribute to the research literature surrounding the uses of neural networks within polymer informatics.</p><p> </p><p>The authors wrote this primer to inspire future engineers and scientists to think creatively about blending computational techniques with empirical engineering insights. This primer is useful for applying machine learning to material sciences and exploring novel polymer composites for next-generation aerospace applications.</p>