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Machine Learning in the Development of Adsorbents for Clean Energy Applications and Greenhouse Gas Capture

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journal contribution
posted on 2023-01-20, 04:08 authored by H Mai, TC Le, D Chen, David WinklerDavid Winkler, RA Caruso

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. 

History

Publication Date

2022-12-28

Journal

Advanced Science

Volume

9

Issue

36

Article Number

2203899

Pagination

22p.

Publisher

Wiley

ISSN

2198-3844

Rights Statement

©2022 The Authors. Advanced Science published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)