Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns

Usman L. Abbas, Yuxuan Zhang, Joseph Tapia, Selim Md, Jin Chen, Jian Shi, Qing Shao

Engineering ›› 2024, Vol. 39 ›› Issue (8) : 74-83.

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Engineering ›› 2024, Vol. 39 ›› Issue (8) : 74-83. DOI: 10.1016/j.eng.2023.10.020
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Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns

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Abstract

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportunities. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics (MD) simulation trajectories. The analysis reveals that DES systems have two unique features compared to non-DES systems: The DESs have ① more imbalance between the numbers of the two intra-component HBs and ② more and stronger inter-component HBs. Based on these results, we develop 30 ML models using ten algorithms and three types of HB-based descriptors. The model performance is first benchmarked using the average and minimal receiver operating characteristic (ROC)-area under the curve (AUC) values. We also analyze the importance of individual features in the models, and the results are consistent with the simulation-based statistical analysis. Finally, we validate the models using the experimental data of 34 systems. The extra trees forest model outperforms the other models in the validation, with an ROC-AUC of 0.88. Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

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Machine learning / Deep eutectic solvents / Molecular dynamics simulations / Hydrogen bond / Molecular design

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Usman L. Abbas, Yuxuan Zhang, Joseph Tapia, Selim Md, Jin Chen, Jian Shi, Qing Shao. Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns. Engineering, 2024, 39(8): 74‒83 https://doi.org/10.1016/j.eng.2023.10.020

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