Issue 41, 2020

Predicting the performance of polyvinylidene fluoride, polyethersulfone and polysulfone filtration membranes using machine learning

Abstract

Micro/ultra/nano-filtration membranes based on polyvinylidene fluoride, polyethersulfone and polysulfone are advancing steadily in laboratory research and scalable production. Variables associated with the composition, fabrication, and operation are highly diverse, and their quantitative correlations with the core performance in permeability, selectivity and their trade-off are still elusive. To predict the performance based on a comprehensive dataset with 1895 vectors, the coefficient of determination spans from 0.79 to 0.85 for regression models, and the area under the receiver operating characteristic curve (AUC) reaches from 0.94 to 0.97 for classification models to distinguish the top 20% (Pareto set) and top 50% (balanced set) membranes with superior performance in the separation of macromolecules and salts from water. Further including experimental structural information (porosity, thickness, surface contact angle and roughness) brings significant improvement for regression models, while filling with predicted values only shows marginal improvement. A standalone algorithm that integrated the predictive models was released at https://github.com/polySML/polySML to facilitate the development of advanced filtration membranes through virtual experiments.

Graphical abstract: Predicting the performance of polyvinylidene fluoride, polyethersulfone and polysulfone filtration membranes using machine learning

Supplementary files

Article information

Article type
Paper
Submitted
04 Aug. 2020
Accepted
01 Okt. 2020
First published
02 Okt. 2020

J. Mater. Chem. A, 2020,8, 21862-21871

Predicting the performance of polyvinylidene fluoride, polyethersulfone and polysulfone filtration membranes using machine learning

T. Liu, L. Liu, F. Cui, F. Ding, Q. Zhang and Y. Li, J. Mater. Chem. A, 2020, 8, 21862 DOI: 10.1039/D0TA07607D

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements