Property prediction for ionic liquids without prior structural knowledge using limited experimental data: A data-driven neural recommender system leveraging transfer learning

• URL: http://arxiv.org/abs/2509.10273v1
• Date: Fri, 12 Sep 2025 14:13:31 GMT
• Title: Property prediction for ionic liquids without prior structural knowledge using limited experimental data: A data-driven neural recommender system leveraging transfer learning
• Authors: Sahil Sethi, Kai Sundmacher, Caroline Ganzer,
• Abstract summary: Ionic liquids (ILs) have emerged as versatile replacements for traditional solvents.<n> accurately predicting key thermophysical properties remains challenging due to the vast chemical design space.<n>We present a data-driven transfer learning framework that enables reliable property prediction for ILs using sparse experimental datasets.
• Score: 0.34410212782758043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ionic liquids (ILs) have emerged as versatile replacements for traditional solvents because their physicochemical properties can be precisely tailored to various applications. However, accurately predicting key thermophysical properties remains challenging due to the vast chemical design space and the limited availability of experimental data. In this study, we present a data-driven transfer learning framework that leverages a neural recommender system (NRS) to enable reliable property prediction for ILs using sparse experimental datasets. The approach involves a two-stage process: first, pre-training NRS models on COSMO-RS-based simulated data at fixed temperature and pressure to learn property-specific structural embeddings for cations and anions; and second, fine-tuning simple feedforward neural networks using these embeddings with experimental data at varying temperatures and pressures. In this work, five essential IL properties are considered: density, viscosity, surface tension, heat capacity, and melting point. The framework supports both within-property and cross-property knowledge transfer. Notably, pre-trained models for density, viscosity, and heat capacity are used to fine-tune models for all five target properties, achieving improved performance by a substantial margin for four of them. The model exhibits robust extrapolation to previously unseen ILs. Moreover, the final trained models enable property prediction for over 700,000 IL combinations, offering a scalable solution for IL screening in process design. This work highlights the effectiveness of combining simulated data and transfer learning to overcome sparsity in the experimental data.

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