Скачать или смотреть Machine learning driven genetic algorithm for designing gas separation polymer

The significance of polymer membranes in CO2 separation lies in their ability to filter CO2 from other gases like nitrogen and oxygen, crucial for addressing climate change. Scientists aim to optimize these membranes for both high permeability and selectivity—traits that often conflict. Advanced techniques like machine learning and genetic algorithms expedite the design process by efficiently analyzing data and generating new polymer combinations. The study utilized Random Forest regression and ECFP4 fingerprinting to accurately predict gas permeability and selectivity. The genetic algorithm identified over 20 novel polymer membranes that surpassed existing performance benchmarks for CO2/N2 and CO2/O2 separations. These new polymers exhibited desirable traits such as high glass transition temperatures and specific functionalities that enhance CO2 interaction, indicating strong potential for practical applications. The framework developed in this study can be generalized to optimize various materials for different separation processes, underscoring the need for improved datasets and active learning to enhance adaptability.
#materialsscience #machinelearning #geneticalgorithm #polymer