What does this research mean for the field?

A hybrid COSMO-RS and machine learning framework utilizing residual learning accurately predicts lignin solubility in deep eutectic solvents, identifying hydrogen bonding, COSMO-RS calculated solubility, and temperature as the dominant governing factors. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to develop a framework for predicting lignin solubility using deep eutectic solvents (DES) combined with machine learning techniques.

May 28, 2026Industrial & Engineering Chemistry Research0 citations

Predictive Design of Deep Eutectic Solvents for Lignin Solubility Using Machine Learning

Predictive Design of Deep Eutectic Solvents for Lignin Solubility: A Machine Learning-Calibrated COSMO-RS Framework

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Authors

HSHuaze Sun TKTianle Kang DCDanyang Cao

Discussion

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Overview

Randomized trial demonstrates high accuracy in lignin solubility prediction using a hybrid COSMO-RS and machine learning framework, highlighting its potential for solvent design.

Key Points

The aim is to develop a framework for predicting lignin solubility using deep eutectic solvents (DES) combined with machine learning techniques.
Developed a hybrid COSMO-RS and machine learning framework for solvent screening.
Utilized residual learning to enhance prediction accuracy for lignin solubility.
Applied multilayer perceptron (MLP) for improved model performance.
Achieved an increase in test R2 from 0.9182 to 0.9941 with MLP.
Reduced average absolute relative deviation (AARD %) to below 8.94%.
Hydrogen bond interactions and COSMO-RS solubility calculations are crucial for solubility predictions.

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Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/6a17dcdf3fad632b0f9d9941 https://doi.org/10.1021/acs.iecr.5c05428

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May 28, 2026Industrial & Engineering Chemistry Research0 citations

Predictive Design of Deep Eutectic Solvents for Lignin Solubility Using Machine Learning

Predictive Design of Deep Eutectic Solvents for Lignin Solubility: A Machine Learning-Calibrated COSMO-RS Framework

View Full Paper

Authors

HSHuaze Sun TKTianle Kang DCDanyang Cao

Discussion

Member takes

Overview

Randomized trial demonstrates high accuracy in lignin solubility prediction using a hybrid COSMO-RS and machine learning framework, highlighting its potential for solvent design.

Key Points

The aim is to develop a framework for predicting lignin solubility using deep eutectic solvents (DES) combined with machine learning techniques.
Developed a hybrid COSMO-RS and machine learning framework for solvent screening.
Utilized residual learning to enhance prediction accuracy for lignin solubility.
Applied multilayer perceptron (MLP) for improved model performance.
Achieved an increase in test R2 from 0.9182 to 0.9941 with MLP.
Reduced average absolute relative deviation (AARD %) to below 8.94%.
Hydrogen bond interactions and COSMO-RS solubility calculations are crucial for solubility predictions.

Ask AI

Helpful

Bookmark

View Full Paper

Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/6a17dcdf3fad632b0f9d9941 https://doi.org/10.1021/acs.iecr.5c05428

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