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High‐Conductivity Electrolytes Screened Using Fragment‐ and Composition‐Aware Deep Learning
Advanced Science, Start page: e21575
Swansea University Authors:
Muyang Chen, Rui Tan
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© 2026 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.
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DOI (Published version): 10.1002/advs.202521575
Abstract
Rising energy generation from renewables (e.g., wind, solar power) will drive global demand for >1.0 TWh of long-duration energy storage by 2030 to stabilise grids and balance supply. Rechargeable batteries are central to this transition, with their performance critically governed by the properti...
| Published in: | Advanced Science |
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| ISSN: | 2198-3844 2198-3844 |
| Published: |
Wiley
2026
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71157 |
| Abstract: |
Rising energy generation from renewables (e.g., wind, solar power) will drive global demand for >1.0 TWh of long-duration energy storage by 2030 to stabilise grids and balance supply. Rechargeable batteries are central to this transition, with their performance critically governed by the properties of active materials and supporting electrolytes. However, designing electrolyte formulations remains a major challenge, as their performance arises from complex, non-additive interactions among lithium salts and organic solvents, requiring elegant molecular design and selection. Conventional trial-and-error strategies still dominate electrolyte design, but they are slow and resource-intensive. Recent machine learning approaches have improved electrolyte screening, yet many rely on coarse molecular representations that neglect fragment-level chemistry and explicit ratios, limiting interpretability and their utility for guiding experiments. Here we introduce a deep learning framework that integrates intermolecular attributions across solvents with intramolecular attributions from functional units. The framework builds a hierarchical representation, decomposing formulations into molecules and their functional units, while integrating ratios, physicochemical descriptors, and salt identity to generate mixture-invariant embeddings for accurate and interpretable conductivity prediction. Applied to benchmark datasets of lithium battery electrolytes, the framework achieves high accuracy in predicting ionic conductivity and enables large-scale virtual screening. Crucially, it provides chemically interpretable insights: fragment-level attentions align with functional units; composition-aware attention reveals the impact of mixing ratios; and counterfactual perturbations confirm causal roles of key motifs. This framework paves the way for data-driven, interpretable electrolyte design and can be generalized to broader formulation challenges in materials science. |
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| Keywords: |
battery electrolyte, data-driven design, graph neural networks, ionic conductivity, machine learning |
| College: |
Faculty of Science and Engineering |
| Funders: |
Royal Society Research Grant (RGS\R2\252134), EPSRC Royce Industrial Collaboration Grant (RICP-R4-100029; ICP5334) and RSC Collaboration Grant (C25-1820146588). |
| Start Page: |
e21575 |