Application of machine learning in polyimide structure design and property regulation

Polyimide (PI) is widely used in modern industry due to its excellent properties. Its synthesis methods and property research have significantly progressed. However, the design and regulation of PI structures through traditional technologies are slow and expensive, which make it difficult to meet the practical demand of modern materials. With the rapid development of high-throughput computing and data-driven technology, machine learning (ML) has become an important method for exploring new materials. Data-driven ML is envisaged as a decisive enabler for new PIs discovery. This paper first introduces the basic workflow and common algorithms of ML. Secondly, applications of ML in material properties prediction, assisting computational simulation technologies and inverse design for desired structures are reviewed. Finally, we discuss the main challenges and possible solutions of ML in PI research.