[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127192-en":3,"doc-seo-127192-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127192,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies","As the chemical industry shifts toward sustainable practices, solvent development increasingly aims to replace fossil-derived solvents with environmentally friendlier options such as ionic liquids (ILs) and deep eutectic solvents (DESs). Artificial intelligence supports discovery and design by enabling physicochemical property prediction and separation process design. This review surveys advances in AI-assisted solvent screening, emphasizing machine learning models and automated high-throughput platforms, and evaluates challenges and future prospects for ML-driven high-throughput strategies in green solvent optimization.","Lawrence Berkeley National Laboratory  \nLBL Publications  \nTitle  \nSolvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies  \nPermalink  \n[https://escholarship.org/uc/item/5v72z23f](https://escholarship.org/uc/item/5v72z23f)  \nJournal  \nChem & Bio Engineering, 2(4)  \nISSN  \n2836-967X  \nAuthors  \nEdaugal, Justin P  \nZhang, Difan Liu, Dupenget al.  \nPublication Date  \n2025-04-24  \nDOI  \n10.1021/cbe.4c00170  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nACS Partner Journal  \nThis article is licensed under CC-BY-NC-ND 4.0  \n[pubs.acs.org/ChemBioEng](pubs.acs.org/ChemBioEng)  Review   \nSolvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies  \nJustin P. Edaugal, Difan Zhang, * Dupeng Liu, * Vassiliki-Alexandra Glezakou, and Ning Sun*  \n Cite This: Chem Bio Eng. 2025, 2, 210−228  \nRead Online  \n\n|  |  |  |  |\n| --- | --- | --- | --- |\n| ACCESS   | Metrics & More |  |  Article Recommendations |\n\nABSTRACT: As the chemical industry shifts toward sustainable practices, there is a growing initiative to replace conventional fossil-derived solvents with environmentally friendly alternatives such as ionic liquids (ILs) and deep eutectic solvents (DESs). Artificial intelligence (AI) plays a key role in the discovery and design of novel solvents and the development of green processes. This review explores the latest advancements in AI-assisted solvent screening with a specific focus on machine learning (ML) models for physicochemical property prediction and separation process design. Additionally, this paper highlights recent progress in the development of automated high-throughput (HT) platforms for solvent screening. Finally, this paper discusses the challenges and prospects of ML-driven HT strategies for green solvent design and optimization. To this end, this review provides key insights to advance solvent screening strategies for future chemical and separation processes.  \nKEYWORDS: Ionic liquids, Deep eutectic solvents, Artificial intelligence, Machine learning, Solvent extraction, High-throughput screening  \n1. INTRODUCTION  \nSeparation processes play an integral role in the production of a diverse range of products across the oil and gas, food, pharmaceutical, and chemical industries. Traditionally, these processes utilize large-scale physical and thermal treatments, such as centrifugation, filtration, and distillation, for the removal of various impurities and byproducts. However, such  \nprocesses are energetically approximately 10−15%  \nintensive and costly, accounting for of global energy consumption. 1  \nSolvent extraction, otherwise known as liquid−liquid extraction (LLE), is a well-established operation for the effective separation of a wide range of synthetic and bio-based compounds from liquid mixtures. By leveraging the differential affinity of target compounds across immiscible liquid phases, solvent extraction can achieve separation with a high extraction efficiency and selectivity. Additionally, solvent extraction offers a simpler process setup and reduced energy costs, positioning  \nit as an attractive strategy for industrial-scale application.  \nDespite these advantages, the efficiency of an extraction process significantly hinges on the selection of the appropriate solvent. The optimal extractant solvent should exhibit a high distribution coefficient, low mutual solubility, low toxicity, high chemical stability, and low cost.2 Most industrial extraction processes rely on conventional organic solvents, owing to their aqueous immiscibility and relatively low bulk costs. However, the environmental concerns regarding their nonrenewable, fossil-fuel ","cbCaibMhdBlVe0kV","https://ap.wps.com/l/cbCaibMhdBlVe0kV","pdf",2365030,3,1,20,"English","en",105,"# Introduction\n## Solvent extraction and separation process role\n## Conventional solvents and sustainability drivers\n## Emerging green alternatives: ILs and DESs\n## Need for faster screening and ML/HT integration","[{\"question\":\"Why is solvent screening important for separation processes?\",\"answer\":\"Separation performance depends strongly on selecting an appropriate solvent, including distribution coefficient, mutual solubility, toxicity, stability, and cost.\"},{\"question\":\"What green solvent alternatives are highlighted in the review?\",\"answer\":\"The review focuses on ionic liquids (ILs) and deep eutectic solvents (DESs), described as tunable and potentially lower-volatility or lower-toxicity alternatives.\"},{\"question\":\"How do machine learning and high-throughput technologies work together in this context?\",\"answer\":\"Machine learning models support physicochemical property prediction and separation process design, while automated high-throughput platforms enable efficient solvent screening at scale.\"}]","Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies | 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is solvent screening important for separation processes?","Question",{"text":76,"@type":77},"Separation performance depends strongly on selecting an appropriate solvent, including distribution coefficient, mutual solubility, toxicity, stability, and cost.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What green solvent alternatives are highlighted in the review?",{"text":81,"@type":77},"The review focuses on ionic liquids (ILs) and deep eutectic solvents (DESs), described as tunable and potentially lower-volatility or lower-toxicity alternatives.",{"name":83,"@type":74,"acceptedAnswer":84},"How do machine learning and high-throughput technologies work together in this context?",{"text":85,"@type":77},"Machine learning models support physicochemical property prediction and separation process design, while automated high-throughput platforms enable efficient solvent screening at 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