Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture. Issue 36 (26th October 2022)

• Record Type: Journal Article
• Authors: Mai, Haoxin; Le, Tu C.; Chen, Dehong; Winkler, David A.; Caruso, Rachel A.
• Is Part Of: Advanced science. Volume 9:Issue 36(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-26
• Subjects: covalent–organic frameworks -- hydrogen -- intermetallics -- metal–organic frameworks -- porous carbons -- porous polymers networks -- zeolites; Science -- Periodicals; 505
• DOI: 10.1002/advs.202203899
• Languages: English
• ISSNs: 2198-3844

Abstract

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. Abstract : This review outlines basic machine learning methods, overviews their use in developing robust adsorbent materials, and provides a perspective on future advances in this research area. The application of machine learning to materials science will continue to bring to light innovative adsorbent materials with potential in future energy storage and conversion applications, gas separation, and greenhouse gas adsorption.