Sanaz Mohammadzadeh Koumleh - School of Chemistry, Shahrood University of Technology
Hamid Hassanpour - faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran.
Masoumeh Esmaeili - University of Applied Sciences, Jahad Daneshgahi of Mashhad, Mashhad, Iran.
Akram Gholami - faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran.

Polymers and polymer composites possess a wide range of applications in chemical, material, and biomedical fields. Although conventional techniques to design and process these significant materials, were successful, they faced important problems. Their synthesis is not only time-consuming but also costly for polymer industries. In recent years, there has been a regenerated hype regarding deep learning, as an approach based on artificial neural networks. Due to the importance of both polymer chemistry and artificial intelligence systems in academic research and industry, it is a requirement to present approaches combining these two promising fields. This paper aims to categorize various deep learning approaches used in the field of polymer science. We expect this can expand the engagement of the polymer chemistry community with artificial intelligence especially deep learning and accelerate the improvement in the data-driven techniques for the synthesis and application of polymers.

Deep Learning, Polymer, Polymer composites, Machine Learning, applications