Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies

作     者：Edaugal, Justin P. Zhang, Difan Liu, Dupeng Glezakou, Vassiliki-Alexandra Sun, Ning 

作者机构：Advanced Biofuels and Bioproducts Process Development Unit Biological Systems and Engineering Division Lawrence Berkeley National Laboratory EmeryvilleCA94608 United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory RichlandWA99354 United States Chemical Sciences Division Oak Ridge National Laboratory Oak RidgeTN37830 United States 

出 版 物：《Chem and Bio Engineering》 (Chem. Bio. Eng.)

年 卷 期：2025年第2卷第4期

页      面：210-228页

核心收录：

基　　金：The authors from the ABPDU acknowledge support from the U.S. Department of Energy\u2019s Bioenergy Technologies Office (BETO)  which is part of the Office of Energy Efficiency and Renewable Energy (EERE)  and funding from the American Recovery and Reinvestment Act. All authors acknowledge the financial support through BETO\u2019s Bioprocessing Separations Consortium. PNNL is operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830. ORNL is operated by UT-Battelle for the U.S. Department of Energy under contract no. DE-AC05-00OR22725. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Neither the United States Government nor any agency thereof  nor any of their employees  makes any warranty  expressed or implied  or assumes any legal liability or responsibility for the accuracy  completeness  or usefulness of any information  apparatus  product  or process disclosed or represents that its use would not infringe privately owned rights 

主　　题：Chemical industry 

摘      要：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. © 2025 The Authors. Co-published by Zhejiang University and American Chemical Society.