Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture
Abstract Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-12-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202203899 |
| _version_ | 1828084590196031488 |
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| author | Haoxin Mai Tu C. Le Dehong Chen David A. Winkler Rachel A. Caruso |
| author_facet | Haoxin Mai Tu C. Le Dehong Chen David A. Winkler Rachel A. Caruso |
| author_sort | Haoxin Mai |
| collection | DOAJ |
| description | Abstract Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high‐performance and low‐cost clean energy applications. This review summarizes basic machine learning methods—data collection, featurization, model generation, and model evaluation—and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature–property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment. |
| first_indexed | 2024-04-11T04:28:14Z |
| format | Article |
| id | doaj.art-742059f150c04267a58b8e557d8f43ff |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-04-11T04:28:14Z |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-742059f150c04267a58b8e557d8f43ff2022-12-29T14:19:16ZengWileyAdvanced Science2198-38442022-12-01936n/an/a10.1002/advs.202203899Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas CaptureHaoxin Mai0Tu C. Le1Dehong Chen2David A. Winkler3Rachel A. Caruso4Applied Chemistry and Environmental Science School of Science STEM College RMIT University Melbourne Victoria 3001 AustraliaSchool of Engineering STEM College RMIT University GPO Box 2476 Melbourne Victoria 3001 AustraliaApplied Chemistry and Environmental Science School of Science STEM College RMIT University Melbourne Victoria 3001 AustraliaMonash Institute of Pharmaceutical Sciences Monash University Parkville VIC 3052 AustraliaApplied Chemistry and Environmental Science School of Science STEM College RMIT University Melbourne Victoria 3001 AustraliaAbstract Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high‐performance and low‐cost clean energy applications. This review summarizes basic machine learning methods—data collection, featurization, model generation, and model evaluation—and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature–property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.https://doi.org/10.1002/advs.202203899covalent–organic frameworkshydrogenintermetallicsmetal–organic frameworksporous carbonsporous polymers networks |
| spellingShingle | Haoxin Mai Tu C. Le Dehong Chen David A. Winkler Rachel A. Caruso Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture Advanced Science covalent–organic frameworks hydrogen intermetallics metal–organic frameworks porous carbons porous polymers networks |
| title | Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture |
| title_full | Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture |
| title_fullStr | Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture |
| title_full_unstemmed | Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture |
| title_short | Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture |
| title_sort | machine learning in the development of adsorbents for clean energy application and greenhouse gas capture |
| topic | covalent–organic frameworks hydrogen intermetallics metal–organic frameworks porous carbons porous polymers networks |
| url | https://doi.org/10.1002/advs.202203899 |
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