Gradient boosting decision tree algorithms for accelerating nanofiltration membrane design and discovery

Interfacial polymerization is the most widely used strategy for nanofiltration membrane fabrication. Despite extensive research on this technology, further improvement in permeance and salt rejection is still essential due to its multidimensional characteristics, including the types of membrane mate...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Desalination Ročník 592; s. 118072
Hlavní autori: Gong, Weijia, Xu, Hangbin, Lu, Jinyan, Kim, Jungbin, Zhao, Yan, Li, Ni, Zhang, Yixuan, Yang, Jiaxuan, Xu, Daliang, Liang, Heng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 21.12.2024
Predmet:
Gradient boosting decision tree algorithms
Interfacial polymerization
Nanofiltration membrane
Permeance
Salt rejection
Nanofiltration membrane
Interfacial polymerization
Permeance
Salt rejection
Gradient boosting decision tree algorithms
ISSN:0011-9164
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Interfacial polymerization is the most widely used strategy for nanofiltration membrane fabrication. Despite extensive research on this technology, further improvement in permeance and salt rejection is still essential due to its multidimensional characteristics, including the types of membrane material and the conditions of membrane optimizing fabrication. Herein, we applied four gradient boosting decision tree algorithms to precisely identify the candidate monomers (represented by the molecular descriptors) and their fabrication conditions. The result of the model evaluation indicated the Extreme Gradient Boosting (XGBoost) algorithm had the best predictive performance in accuracy and generalization in predicting membrane permeance and salt rejection, with the corresponding determination coefficients on the test set being 0.76 and 0.88. Shapley additive explanation analysis showed that the aqueous monomer concentration was the most influential fabrication condition in membrane performance. Besides, the partition coefficient (Log P) and topological polar surface area were the most important molecular descriptors in water permeance and salt rejection, respectively. Overall, this study proposed innovative machine learning algorithms to disentangle the multidimensional interactions of various influencing factors on membrane performance, thus initiating a paradigm shift in the development of high-performance nanofiltration membranes. [Display omitted] •Gradient boosting decision tree algorithms accurately predict membrane performance.•Molecular descriptors are used to represent the candidate monomers.•Aqueous monomer concentration is the most important feature.•Integration of monomers and fabrication conditions for the membrane design is highlighted.
ISSN:0011-9164
DOI:10.1016/j.desal.2024.118072