Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns
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 opportuni- ties. 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 oper- ating characteristic (ROC)-area under the curve (AUC) values. We also analyze the importance of individ- ual 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 impor- tance of HBs in DES formation and shows the potential of ML in discovering new DESs.
Document Type
Article
Publication Date
2024
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.eng.2023.10.020
Funding Information
This work was supported by Ignite Research Collaborations (IRC), Startup funds, and the UK Artificial Intelligence (AI) in Med- icine Research Alliance Pilot (NCATS UL1TR001998 and NCI P30 CA177558), University of Kentucky Center for Computational Sciences and Information Technology Services Research Computing for the use of the Lipscomb Compute Cluster of the University of Kentucky.
Repository Citation
Abbas, Usman Lame; Zhang, Yuxuan; Tapia, Joseph; Selim, Md; Chen, Jin; Shi, Jian; and Shao, Qing, "Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns" (2024). Markey Cancer Center Faculty Publications. 264.
https://uknowledge.uky.edu/markey_facpub/264
Included in
Environmental Engineering Commons, Oncology Commons, Other Chemical Engineering Commons, Other Computer Sciences Commons, Other Engineering Commons, Other Materials Science and Engineering Commons, Power and Energy Commons
Notes/Citation Information
Ó 2024 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).