Polymer design for solvent separations by integrating simulations, experiments and known physics via machine learning
Janhavi Nistane, Rohan Datta, Young‐Joo Lee, Harikrishna Sahu, Seung Soon Jang, Ryan P. Lively, Rampi Ramprasad
Abstract
This study guides the discovery of sustainable high-performance polymer membranes for organic binary solvent separations. We focus on solvent diffusivity in polymers, a key factor in quantifying solvent transport. Traditional experimental and computational methods for determining diffusivity are time- and resource-intensive, while current machine learning (ML) models often lack accuracy outside their training domains. To overcome this, we fuse experimental and simulated diffusivity data to train physics-enforced multi-task ML models, achieving more robust predictions in unseen chemical spaces and outperforming single-task models in data-limited scenarios. Next, we address the challenge of identifying optimal membranes for a model toluene-heptane separation, identifying polyvinyl chloride (PVC) as the optimal membrane among 13,000 polymers, consistent with literature findings, thereby validating our methodology. Expanding our search, we screen 1 million publicly available and 7 million chemically recyclable polymers, identifying greener halogen-free alternatives to PVC. This capability is expected to advance membrane design for solvent separations. Guiding membrane design for organic solvent separations through a physics-enforced multi-task ML model fusing experimental and simulated data.