Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization.

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Science

Background:

• Porous membranes, including polymeric and 2D materials, are crucial for applications like water filtration.
• Machine learning (ML) has shown significant success in scientific discovery and is increasingly applied to membrane design.
• Data-driven approaches are transforming the traditional methods of membrane research.

Purpose of the Study:

• To review the applications of ML in porous membrane design.
• To categorize ML use into property prediction, insight generation, and guided design.
• To discuss current challenges and future prospects of ML in this field.

Main Methods:

• Literature review of ML applications in membrane science.
• Categorization of ML techniques based on their role in membrane design (prediction, explanation, guidance).
• Analysis of existing research to identify trends, successes, and limitations.

Main Results:

• ML is effectively used for predicting membrane properties.
• Explainable AI (XAI) enables quantitative relationships between properties and performance.
• ML facilitates the design, optimization, and virtual screening of novel membranes.

Conclusions:

• ML offers powerful tools for accelerating porous membrane discovery and optimization.
• Addressing current challenges in data availability and model interpretability is key for future progress.
• The integration of ML is poised to significantly advance membrane technology for various applications.