Deep learning-enabled discovery of low-melting-point ionic liquids
File(s)
Published online version
Supporting information
Author(s)
Ren, Gaopeng
Mroz, Austin
Philippi, Frederik
Welton, Tom
Jelfs, Kim
Type
Journal Article
Abstract
Ionic liquids (ILs) are salts that are liquids at ambient conditions (typically below 373 K) and are known for their many unique properties, including low volatility and high thermal stability. Despite the promise of ILs, their targeted design is challenging for several reasons, including (i) the vast number of candidate ions that could be synthesised as components of an IL, (ii) the lack of predictive methods to determine what ion combinations will yield ILs with desired melting points, and (iii) experimentally known ILs possess limited chemical diversity. In this work, we present a data-driven framework for designing novel low-melting-point ILs. We model ILs as bipartite graphs and apply a link prediction algorithm to identify promising cation–anion pairs, expanding the collected IL database more than 30-fold, while prioritising low melting points. To further explore chemical space, we trained variational auto-encoders (VAEs) to generate new IL candidates through learning a latent space that enables modelling the data distribution. A thermodynamics-inspired classification model is subsequently employed to filter out ILs predicted to melt above 373 K. Finally, molecular dynamics simulations validate our approach, confirming that 18 out of 20 generated ILs have melting points below 373 K.
Date Issued
2026-01-21
Date Acceptance
2026-01-20
Citation
Digital Discovery, 2026
ISSN
2635-098X
Publisher
The Royal Society of Chemistry
Journal / Book Title
Digital Discovery
Copyright Statement
© 2026 The Author(s). Published by the Royal Society of Chemistry. This article is licensed under aCreative Commons Attribution 3.0 Unported Licence (https://creativecommons.org/licenses/by/3.0/)
License URL
Identifier
10.1039/d5dd00282f
Publication Status
Published online
Date Publish Online
2026-01-21