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

Justin P. Edaugal; Difan Zhang; Dupeng Liu; Vassiliki Alexandra Glezakou; Ning Sun

doi:10.1021/cbe.4c00170

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

Justin P. Edaugal, Difan Zhang, Dupeng Liu, Vassiliki Alexandra Glezakou, Ning Sun

Chemical Sciences Division

Research output: Contribution to journal › Review article › peer-review

11 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	210-228
Number of pages	19
Journal	Chem and Bio Engineering
Volume	2
Issue number	4
DOIs	https://doi.org/10.1021/cbe.4c00170
State	Published - Apr 24 2025

Funding

The authors from the ABPDU acknowledge support from the U.S. Department of Energy’s 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’s 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.

Keywords

Artificial intelligence
Deep eutectic solvents
High-throughput screening
Ionic liquids
Machine learning
Solvent extraction

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10.1021/cbe.4c00170

Cite this

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title = "Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies",

abstract = "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.",

keywords = "Artificial intelligence, Deep eutectic solvents, High-throughput screening, Ionic liquids, Machine learning, Solvent extraction",

author = "Edaugal, \{Justin P.\} and Difan Zhang and Dupeng Liu and Glezakou, \{Vassiliki Alexandra\} and Ning Sun",

note = "Publisher Copyright: {\textcopyright} 2025 The Authors. Co-published by Zhejiang University and American Chemical Society.",

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doi = "10.1021/cbe.4c00170",

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T1 - Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies

AU - Edaugal, Justin P.

AU - Zhang, Difan

AU - Liu, Dupeng

AU - Glezakou, Vassiliki Alexandra

AU - Sun, Ning

PY - 2025/4/24

Y1 - 2025/4/24

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Deep eutectic solvents

KW - High-throughput screening

KW - Ionic liquids

KW - Machine learning

KW - Solvent extraction

UR - https://www.scopus.com/pages/publications/105003454784

U2 - 10.1021/cbe.4c00170

DO - 10.1021/cbe.4c00170

M3 - Review article

AN - SCOPUS:105003454784

SN - 2836-967X

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SP - 210

EP - 228

JO - Chem and Bio Engineering

JF - Chem and Bio Engineering

IS - 4

ER -

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

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Keywords

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