Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.

ABSTRACT: Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility and high tunability. Traditional trial-and-error methods for material synthesis are inadequate to meet the growing demands for high-performance membranes. Machine learning (ML) has demonstrated huge potential to accelerate design and discovery of membrane materials. In this review, we cover strengths and weaknesses of the traditional methods, followed by a discussion on the emergence of ML for developing advanced polymeric membranes. We describe methodologies for data collection, data preparation, the commonly used ML models, and the explainable artificial intelligence (XAI) tools implemented in membrane research. Furt

SUBMITTER: Dangayach R 

PROVIDER: S-EPMC11755723 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature