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.Tradit...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.展开更多
基金the Office of Naval Research through a multidisciplinary university research initiative(MURI)for their funding support,We would also like to acknowledge Dr.Kuan-Hsuan Shen for their valuable support in building the simulation pipeline.We also extend a thank you to Dr.Lihua Chen for her guidance in the initial stage of the work.This research is supported in part through research cyber-infrastructure resources and services provided by the Partnership for an Advanced Computing Environment(PACE)at the Georgia Institute of Technology and XSEDE/ACCESS for computational support through Grant No.TG-DMR080058N.
文摘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.
