Data-Driven Development of Deep Eutectic Solvents: A Review of Machine Learning in Property Prediction and Rational Design

Zhen Yang; Xingchi Deng; Jin Shu; Yichi Zhang; Shengtao He

doi:10.6919/ICJE.202606_12(6).0011

Authors

Zhen Yang
Xingchi Deng
Jin Shu
Yichi Zhang
Shengtao He

DOI:

https://doi.org/10.6919/ICJE.202606_12(6).0011

Keywords:

Deep Eutectic Solvents (DES); Machine Learning; Property Prediction; Inverse Design; Multi-objective Optimization; Data-driven.

Abstract

Deep Eutectic Solvents (DESs) represent a novel class of emerging green solvents formed through hydrogen-bonding interactions between hydrogen bond donors (HBDs) and hydrogen bond acceptors (HBAs). Boasting core advantages such as facile synthesis, low cost, environmental benignity, and exceptional structural and functional designability, DESs have demonstrated substantial application value and industrial potential across various sectors, including natural product extraction, biomass valorization, hydrometallurgy, acidic gas capture, and electrochemical energy storage. However, several fundamental scientific and technical bottlenecks-including the combinatorial explosion of the chemical design space arising from vast HBD/HBA combinations, the elusive structure-property relationships between microscopic intermolecular interactions and macroscopic physicochemical properties, and the challenges in synergistically optimizing multiple performance indicators in complex application scenarios-have rendered traditional "trial-and-error" R&D models inadequate for the targeted rational design of DESs, thereby significantly impeding their large-scale industrialization. As a data-driven intelligent research paradigm, machine learning (ML) provides a transformative technical route to address these challenges by leveraging its robust capabilities in high-dimensional feature mining, complex non-linear relationship fitting, and global optimization. This review systematically delineates the latest domestic and international advancements in ML-enabled DES research. First, it elucidates the core principles and mainstream methodologies for molecular descriptor selection and high-quality dataset construction in DES property prediction. Second, it summarizes model architectures, algorithmic optimizations, and predictive performance of ML regarding the thermophysical properties as well as chemical and biological activities of DESs. Subsequently, it highlights technical implementations and application outcomes of ML in the intelligent prediction of DES formation, application-oriented reverse design, and multi-objective optimization of extraction and separation processes. Finally, the review provides an in-depth analysis of the core challenges currently facing the field-such as the lack of standardized datasets, insufficient model interpretability, and weak mechanistic insights-while offering perspectives on future research directions. This review aims to provide a systematic theoretical framework and research roadmap for the intelligent rational design, precise property modulation, and industrial application of DESs, facilitating the paradigm shift in green solvent development from traditional empirical trial-and-error toward a data-driven intelligent paradigm.

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