Machine learning aided evaluation and design based on polymer membrane materials

Abstract

With the acceleration of the global modern industrialisation process and the increasingly serious environmental problems, the development of low energy consumption, high efficiency electrochemical energy conversion equipment and separation system has become a research hotspot in the scientific and industrial circles. At present, machine learning has become an important research method to explore and expand two-dimensional material family. Traditional experimental and computational methods have low fault tolerance when studying two-dimensional materials, which requires a lot of time and research and development costs. Machine learning, due to its powerful data processing capability and flexible algorithm model, can help reduce the time and cost of discovering and understanding two-dimensional materials, and can effectively predict and expand two-dimensional material systems based on data and explore their potential for experimental synthesis and application. This paper will focus on the methods of machine learning, the exploration of machine learning in 2D material design and synthesis, and the exploration of machine learning in 2D material properties and applications. Finally, this paper uses ML algorithm to test the synthesised polymer. The experimental data points and prediction data points have relatively good consistency with each other, which indicates that ML model can be used as a prediction tool to identify the undeveloped polymer for gas separation.