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Machine learning for layer-by-layer nanofiltration membrane performance prediction and polymer candidate exploration.
- Source :
-
Chemosphere [Chemosphere] 2024 Feb; Vol. 350, pp. 140999. Date of Electronic Publication: 2023 Dec 25. - Publication Year :
- 2024
-
Abstract
- In this study, machine learning-based models were established for layer-by-layer (LBL) nanofiltration (NF) membrane performance prediction and polymer candidate exploration. Four different models, i.e., linear, random forest (RF), boosted tree (BT), and eXtreme Gradient Boosting (XGBoost), were formed, and membrane performance prediction was determined in terms of membrane permeability and selectivity. The XGBoost exhibited optimal prediction accuracy for membrane permeability (coefficient of determination (R <superscript>2</superscript> ): 0.99) and membrane selectivity (R <superscript>2</superscript> : 0.80). The Shapley Additive exPlanation (SHAP) method was utilized to evaluate the effects of different LBL NF membrane fabrication conditions on membrane performances. The SHAP method was also used to identify the relationships between polymer structure and membrane performance. Polymers were represented by Morgan fingerprint, which is an effective description approach for developing modeling. Based on the SHAP value results, two reference Morgan fingerprints were constructed containing atomic groups with positive contributions to membrane permeability and selectivity. According to the reference Morgan fingerprint, 204 potential polymers were explored from the largest polymer database (PoLyInfo). By calculating the similarities between each potential polymer and both reference Morgan fingerprints, 23 polymer candidates were selected and could be further used for LBL NF membrane fabrication with the potential for providing good membrane performance. Overall, this work provided new ways both for LBL NF membrane performance prediction and high-performance polymer candidate exploration. The source code for the models and algorithms used in this study is publicly available to facilitate replication and further research. https://github.com/wangliwfsd/LLNMPP/.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2023 Elsevier Ltd. All rights reserved.)
- Subjects :
- Membranes
Databases, Factual
Polymers
Algorithms
Machine Learning
Subjects
Details
- Language :
- English
- ISSN :
- 1879-1298
- Volume :
- 350
- Database :
- MEDLINE
- Journal :
- Chemosphere
- Publication Type :
- Academic Journal
- Accession number :
- 38151066
- Full Text :
- https://doi.org/10.1016/j.chemosphere.2023.140999