Edaugal Justin P, Zhang Difan, Liu Dupeng, Glezakou Vassiliki-Alexandra, Sun Ning
Advanced Biofuels and Bioproducts Process Development Unit, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Emeryville, California 94608, United States.
Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.
Chem Bio Eng. 2025 Mar 5;2(4):210-228. doi: 10.1021/cbe.4c00170. eCollection 2025 Apr 24.
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.
随着化学工业向可持续发展实践转变，越来越多的倡议旨在用离子液体（ILs）和深共熔溶剂（DESs）等环境友好型替代品取代传统的化石衍生溶剂。人工智能（AI）在新型溶剂的发现与设计以及绿色工艺的开发中发挥着关键作用。本综述探讨了人工智能辅助溶剂筛选的最新进展，特别关注用于物理化学性质预测和分离过程设计的机器学习（ML）模型。此外，本文强调了用于溶剂筛选的自动化高通量（HT）平台开发方面的最新进展。最后，本文讨论了机器学习驱动的高通量策略在绿色溶剂设计和优化方面的挑战与前景。为此，本综述提供了关键见解，以推动未来化学和分离过程的溶剂筛选策略。
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