Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, Melbourne, Victoria, 3001, Australia.
School of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria, 3001, Australia.
Adv Sci (Weinh). 2022 Dec;9(36):e2203899. doi: 10.1002/advs.202203899. Epub 2022 Oct 26.
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.
解决温室气体水平降低的气候变化挑战需要用于清洁能源应用的创新吸附剂材料。机器学习的最新进展刺激了在发现、设计和部署具有高性能和低成本清洁能源应用潜力的材料方面的技术突破。这篇综述总结了基本的机器学习方法——数据收集、特征化、模型生成和模型评估——并回顾了它们在开发稳健吸附剂材料中的应用。提供了关键案例研究，其中这些方法用于加速吸附剂材料的设计和发现、优化合成条件以及理解复杂的特征-性能关系。该综述为希望使用机器学习方法快速开发对环境有积极影响的有效吸附剂材料的研究人员提供了一个简明的资源。
