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Ions are atoms or molecules bearing an electrical charge. A cation (a positive ion) forms when a neutral atom loses one or more electrons from its valence shell, and an anion (a negative ion) forms when a neutral atom gains one or more electrons in its valence shell. Compounds composed of ions are called ionic compounds (or salts), and their constituent ions are held together by ionic bonds: electrostatic forces of attraction between oppositely charged cations and anions. 
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Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques
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Drawing a materials map with an autoencoder for lithium ionic conductors.

Yudai Yamaguchi1, Taruto Atsumi1, Kenta Kanamori2

  • 1Department of Advanced Ceramics, Nagoya Institute of Technology, Gokiso, Showa, Nagoya, Aichi, 466-8555, Japan.

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|October 5, 2023
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Summary
This summary is machine-generated.

This study introduces a novel materials informatics approach, combining machine learning with researcher intuition to discover new functional materials. A unique two-dimensional materials map aids in identifying novel lithium-ion conductive materials beyond existing databases.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Data Science

Background:

  • Traditional functional material discovery relies on researcher expertise.
  • Materials informatics (MI) offers systematic exploration but faces challenges in novel material discovery.
  • Optimizing existing materials is crucial for technological advancement.

Purpose of the Study:

  • To develop a novel method for discovering new materials beyond current databases.
  • To integrate materials informatics with researcher intuition for efficient material exploration.
  • To enhance the discovery of advanced functional materials, specifically for lithium-ion conductivity.

Main Methods:

  • Developed a two-dimensional materials map using an autoencoder-based neural network.
  • Plotted known materials data based on composition and structure.
  • Focused on 708 lithium oxide materials, analyzing conductivity and migration energy (ME).

Main Results:

  • The materials map effectively visualizes the distribution of existing materials data.
  • The approach facilitates intuitive identification of potential new materials.
  • Demonstrated the correlation between material properties and migration energy for lithium-ion conductivity.

Conclusions:

  • The proposed method enhances researcher intuition for discovering novel functional materials.
  • The materials map is a valuable tool for exploring materials outside existing databases.
  • This approach can accelerate the development of new lithium-ion conductive materials.