Methodological Roadmap for Machine Learning in Deep Eutectic Solvent Research: A Framework-Driven Review and Perspective

Deep eutectic solvents (DESs) are tunable green solvents whose complex physicochemical properties make experimental optimization challenging. While machine learning (ML) offers a powerful alternative to inefficient traditional methods, its application is often hindered by challenges in data quality, molecular representation, and model generalization. This work moves beyond a conventional review to propose a holistic methodological framework for applying ML to DES research. We deconstruct the process into six core dimensions: data set construction, molecular representation, modeling mechanisms, model evaluation, interpretability, and user interfaces. This framework serves as a practical roadmap for building interpretable, reliable, and deployable ML models, aiming to accelerate the intelligent design of DESs and foster a shift from empirical discovery to a more principled, data-driven approach.