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An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:
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Conductivity prediction model for ionic liquids using machine learning.

R Datta1, R Ramprasad2, S Venkatram2

  • 1The Galloway School, Atlanta, Georgia 30327, USA.

The Journal of Chemical Physics
|June 8, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a deep neural network to predict ionic liquid (IL) conductivity, accelerating the discovery of new materials for applications like energy storage. The model uses diverse chemical data for accurate and rapid conductivity assessments.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Electrochemistry

Background:

  • Ionic liquids (ILs) are versatile salts with applications in energy storage, solar cells, and sensors due to high ionic conductivity and thermal stability.
  • Traditional physical methods for measuring IL conductivity are time-consuming and costly.
  • Computational methods offer a rapid and effective alternative for screening IL properties.

Purpose of the Study:

  • To develop a deep neural network (DNN) model for rapid and accurate prediction of ionic liquid conductivity.
  • To create one of the most chemically diverse conductivity prediction models to date.
  • To identify key chemo-structural features influencing ionic conductivity for designing new ILs.

Main Methods:

  • Constructed a DNN model trained on 406 experimentally measured and published ionic liquid conductivity data points.
  • Employed feature engineering to identify significant chemo-structural descriptors.
  • Utilized a diverse dataset encompassing 406 unique and chemically varied ionic liquids.

Main Results:

  • Achieved rapid and accurate predictions of ionic liquid conductivity using the developed DNN model.
  • Identified key features correlating with ionic conductivity, serving as design guidelines for new ILs.
  • Demonstrated improved performance over previous models constrained by data availability or IL diversity.

Conclusions:

  • Machine learning, specifically DNNs, holds significant potential for accelerating the discovery and testing of high-conductivity ionic liquids.
  • The developed model and identified features can guide the rational design of tailored ionic liquids for specific applications.
  • This approach offers a cost-effective and efficient alternative to experimental measurements for IL conductivity screening.