Accelerating the Design of New Natural Deep Eutectic Solvents with Supervised Variational Autoencoders
Year: 2026
Type: preprint
Abstract: The rational design of natural deep eutectic solvents (NaDESs) is hindered by the vast combinatorial space arising from the possible combinations of hydrogen bond donors and acceptors. This enormous design space limits the systematic experimental exploration of their physicochemical properties. In this work, we propose a supervised variational autoencoder (SVAE) framework to efficiently explore this chemical space by coupling generative modeling with property prediction. The model is trained on a curated dataset of deep eutectic solvents to learn simultaneously the latent structural representations and correlations with target properties, including melting point, density, and viscosity. The SVAE generates virtual NaDES candidates characterized by molecular descriptors, component ratios, and target property values. To associate these virtual candidates with real molecules, a descriptor matching strategy is applied using the COCONUT natural product database. The resulting NaDES candidates are subsequently validated using predictive models trained on experimental DES data. The results show strong agreement between the SVAE-generated target property values and the corresponding predictions of the regression models (R2 = 0.89, RMSE=21.7 K for melting point; R2 = 0.91, RMSE = 0.04 g·cm-3 for density; and R2 = 0.89, RMSE=0.47 cP for ln(viscosity)). This workflow demonstrates how guided generative models can be combined with machine learning models to accelerate the discovery of sustainable solvents. Beyond NaDESs, the proposed strategy provides a general and transferable framework for the rational design of other chemical systems, where combinatorial complexity limits conventional experimental approaches.
Source: ChemRxiv
Institution: Unité de Catalyse et Chimie du Solide
Language: English
Subfield: Physical and Theoretical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7212072920