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Mai H, Le TC, Chen D, Winkler DA, Caruso RA. Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203899. [PMID: 36285802 PMCID: PMC9798988 DOI: 10.1002/advs.202203899] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/27/2022] [Indexed: 06/04/2023]
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.
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Affiliation(s)
- Haoxin Mai
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
| | - Tu C. Le
- School of EngineeringSTEM CollegeRMIT UniversityGPO Box 2476MelbourneVictoria3001Australia
| | - Dehong Chen
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
| | - David A. Winkler
- Monash Institute of Pharmaceutical SciencesMonash UniversityParkvilleVIC3052Australia
- School of Biochemistry and ChemistryLa Trobe UniversityKingsbury DriveBundoora3042Australia
- School of PharmacyUniversity of NottinghamNottinghamNG7 2RDUK
| | - Rachel A. Caruso
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
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Zhang Z, Cheng M, Xiao X, Bi K, Song T, Hu KQ, Dai Y, Zhou L, Liu C, Ji X, Shi WQ. Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr. ACS APPLIED MATERIALS & INTERFACES 2022; 14:33076-33084. [PMID: 35801670 DOI: 10.1021/acsami.2c05272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Separation of Cs/Sr is one of many coordination-chemistry-centered processes in the grand scheme of spent nuclear fuel reprocessing, a critical link for a sustainable nuclear energy industry. To deploy a crystallizing Cs/Sr separation technology, we planned to systematically screen and identify candidate ligands that can efficiently and selectively bind to Sr2+ and form coordination polymers. Therefore, we mined the Cambridge Structural Database for characteristic structural information and developed a machine-learning-guided methodology for ligand evaluation. The optimized machine-learning model, correlating the molecular structures of the ligands with the predicted coordinative properties, generated a ranking list of potential compounds for Cs/Sr selective crystallization. The Sr2+ sequestration capability and selectivity over Cs+ of the promising ligands identified (squaric acid and chloranilic acid) were subsequently confirmed experimentally, with commendable performances, corroborating the artificial-intelligence-guided strategy.
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Affiliation(s)
- Zhiyuan Zhang
- School of Chemical Engineering, Sichuan University, Chengdu 610065, People's Republic of China
| | - Min Cheng
- School of Chemical Engineering, Sichuan University, Chengdu 610065, People's Republic of China
| | - Xinyi Xiao
- School of Chemical Engineering, Sichuan University, Chengdu 610065, People's Republic of China
| | - Kexin Bi
- School of Chemical Engineering, Sichuan University, Chengdu 610065, People's Republic of China
| | - Ting Song
- School of Chemical Engineering, Sichuan University, Chengdu 610065, People's Republic of China
| | - Kong-Qiu Hu
- Laboratory of Nuclear Energy Chemistry, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Yiyang Dai
- School of Chemical Engineering, Sichuan University, Chengdu 610065, People's Republic of China
| | - Li Zhou
- School of Chemical Engineering, Sichuan University, Chengdu 610065, People's Republic of China
| | - Chong Liu
- School of Chemical Engineering, Sichuan University, Chengdu 610065, People's Republic of China
| | - Xu Ji
- School of Chemical Engineering, Sichuan University, Chengdu 610065, People's Republic of China
| | - Wei-Qun Shi
- Laboratory of Nuclear Energy Chemistry, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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