Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects
Organic framework membranes (OFMs) have emerged as transformative materials for separation technologies due to their tunable porosity, structural diversity, and stability, yet their design and optimization face challenges in navigating vast chemical spaces and complex performance trade-offs. This re...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Membranes |
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| Online Access: | https://www.mdpi.com/2077-0375/15/6/178 |
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| author | Tong Wu Jiawei Zhang Qinghao Yan Jingxiang Wang Hao Yang |
| author_facet | Tong Wu Jiawei Zhang Qinghao Yan Jingxiang Wang Hao Yang |
| author_sort | Tong Wu |
| collection | DOAJ |
| description | Organic framework membranes (OFMs) have emerged as transformative materials for separation technologies due to their tunable porosity, structural diversity, and stability, yet their design and optimization face challenges in navigating vast chemical spaces and complex performance trade-offs. This review highlights the pivotal role of machine learning (ML) in overcoming these limitations by integrating multi-source data, constructing quantitative structure–property relationships, and enabling the cross-scale optimization of OFMs. Methodologically, ML workflows—spanning data construction, feature engineering, and model optimization—accelerate candidate screening, inverse design, and mechanistic interpretation, as demonstrated in gas separations and nascent liquid-phase applications. Key findings reveal that ML identifies critical structural descriptors and environmental parameters, guiding the development of high-performance membranes that surpass traditional selectivity–permeability limits. Challenges persist in liquid separations due to dynamic operational complexities and data scarcity, while emerging frameworks offer untapped potential. The integration of interpretable ML, in situ characterization, and industrial scalability strategies is essential to transition OFMs from laboratory innovations to sustainable, adaptive separation systems. This review underscores ML’s transformative capacity to bridge computational insights with experimental validation, fostering next-generation membranes for carbon neutrality, water security, and energy-efficient industrial processes. |
| format | Article |
| id | doaj-art-31cc12a449124297afd7dd5eeabd7bab |
| institution | Kabale University |
| issn | 2077-0375 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Membranes |
| spelling | doaj-art-31cc12a449124297afd7dd5eeabd7bab2025-08-20T03:27:40ZengMDPI AGMembranes2077-03752025-06-0115617810.3390/membranes15060178Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial ProspectsTong Wu0Jiawei Zhang1Qinghao Yan2Jingxiang Wang3Hao Yang4State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, ChinaOrganic framework membranes (OFMs) have emerged as transformative materials for separation technologies due to their tunable porosity, structural diversity, and stability, yet their design and optimization face challenges in navigating vast chemical spaces and complex performance trade-offs. This review highlights the pivotal role of machine learning (ML) in overcoming these limitations by integrating multi-source data, constructing quantitative structure–property relationships, and enabling the cross-scale optimization of OFMs. Methodologically, ML workflows—spanning data construction, feature engineering, and model optimization—accelerate candidate screening, inverse design, and mechanistic interpretation, as demonstrated in gas separations and nascent liquid-phase applications. Key findings reveal that ML identifies critical structural descriptors and environmental parameters, guiding the development of high-performance membranes that surpass traditional selectivity–permeability limits. Challenges persist in liquid separations due to dynamic operational complexities and data scarcity, while emerging frameworks offer untapped potential. The integration of interpretable ML, in situ characterization, and industrial scalability strategies is essential to transition OFMs from laboratory innovations to sustainable, adaptive separation systems. This review underscores ML’s transformative capacity to bridge computational insights with experimental validation, fostering next-generation membranes for carbon neutrality, water security, and energy-efficient industrial processes.https://www.mdpi.com/2077-0375/15/6/178machine learningorganic framework membranesgas separationliquid separationmetal–organic framework membranescovalent organic framework membranes |
| spellingShingle | Tong Wu Jiawei Zhang Qinghao Yan Jingxiang Wang Hao Yang Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects Membranes machine learning organic framework membranes gas separation liquid separation metal–organic framework membranes covalent organic framework membranes |
| title | Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects |
| title_full | Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects |
| title_fullStr | Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects |
| title_full_unstemmed | Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects |
| title_short | Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects |
| title_sort | machine learning in the design and performance prediction of organic framework membranes methodologies applications and industrial prospects |
| topic | machine learning organic framework membranes gas separation liquid separation metal–organic framework membranes covalent organic framework membranes |
| url | https://www.mdpi.com/2077-0375/15/6/178 |
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