Exploring the deviation from Nernst-Einstein conductivity in ionic liquids using machine learning

Aditi Seshadri; L. D. Hess; Shuwen Yue

doi:10.1039/d5dd00414d

January 1, 2025Digital DiscoveryOpen Access

Exploring the deviation from Nernst-Einstein conductivity in ionic liquids using machine learning

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Key Points

The aim is to investigate how ionic liquids deviate from expected conductivity models using machine learning techniques.
Analyzed various ionic liquids for conductivity properties
Applied machine learning to identify patterns and deviations
Evaluated the influence of chemical composition on conductivity
Identified significant deviations from Nernst-Einstein predictions
Machine learning models improved understanding of ionic liquid behaviors
Provided insights for potential composition adjustments to enhance conductivity

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Implication

Exploration reveals insights on ionic conductivity in ionic liquids, indicating potential for improved battery performance.

Abstract

Ionic liquids (ILs) are promising candidates for safer battery electrolytes due to their low flammability, but their low ionic conductivity constrains battery performance. Given the large chemical space of potential...

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Cite This Study

Seshadri et al. (2025) studied this question.

synapsesocial.com/papers/69b2583896eeacc4fcec7a66 https://doi.org/10.1039/d5dd00414d

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Exploring the deviation from Nernst-Einstein conductivity in ionic liquids using machine learning

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Exploring the deviation from Nernst-Einstein conductivity in ionic liquids using machine learning

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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