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

Justin P Edaugal¹, Difan Zhang², Dupeng Liu¹

¹Advanced Biofuels and Bioproducts Process Development Unit, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Emeryville, California 94608, United States.

Chem & Bio Engineering

|April 30, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) accelerates the discovery of green solvents like ionic liquids (ILs) and deep eutectic solvents (DESs). Machine learning models and automated platforms are key to predicting properties and designing sustainable chemical processes.

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Area of Science:

Green Chemistry
Computational Chemistry
Chemical Engineering

Background:

The chemical industry is transitioning towards sustainable practices, necessitating the replacement of traditional fossil-derived solvents.
Ionic liquids (ILs) and deep eutectic solvents (DESs) are emerging as promising environmentally friendly alternatives.
Artificial intelligence (AI) is increasingly vital for developing novel solvents and green chemical processes.

Purpose of the Study:

To review the latest advancements in AI-assisted solvent screening.
To focus on machine learning (ML) models for predicting physicochemical properties and designing separation processes.
To highlight progress in automated high-throughput (HT) platforms for solvent screening.

Main Methods:

Review of recent literature on AI applications in solvent discovery and design.
Analysis of machine learning models used for physicochemical property prediction.
Examination of automated high-throughput screening platforms for ILs and DESs.

Main Results:

AI, particularly ML, significantly enhances the prediction of solvent properties and the design of separation processes.
Automated HT platforms are accelerating the screening and identification of novel green solvents.
Integration of ML with HT strategies offers a powerful approach for green solvent design.

Conclusions:

AI-driven solvent screening is crucial for advancing sustainable chemical practices.
ML models and automated HT platforms are essential tools for discovering and optimizing green solvents.
Further development in ML-driven HT strategies will shape the future of chemical and separation processes.