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Keitaro Sodeyama1, Yasuhiko Igarashi, Tomofumi Nakayama

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Summary

We evaluated data-driven methods for predicting liquid electrolyte properties for lithium-ion batteries. Exhaustive search with linear regression (ES-LiR) proved most accurate and efficient, balancing prediction accuracy and computational cost.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Electrochemistry

Background:

  • Developing high-performance lithium-ion batteries requires novel liquid electrolyte materials.
  • Data-driven techniques are underutilized for studying disordered liquid solutions compared to solid materials.

Purpose of the Study:

  • To assess the accuracy and efficiency of multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR) for predicting liquid properties.
  • To identify the most effective data-driven technique for electrolyte material discovery.

Main Methods:

  • Coordination energy and melting point were used as test properties for liquid materials.
  • Three information techniques (MLR, LASSO, ES-LiR) were compared based on estimation accuracy and efficiency.
  • A weight diagram of descriptors was utilized to analyze the relationship between prediction accuracy and calculation cost.

Main Results:

  • ES-LiR demonstrated the highest estimation accuracy among the evaluated techniques.
  • ES-LiR effectively illustrates the trade-off between prediction accuracy and computational cost.
  • The technique enables informed selection of accuracy-cost balance for large-scale material screening.

Conclusions:

  • ES-LiR is a superior method for predicting liquid electrolyte properties in battery research.
  • This approach facilitates efficient discovery of new materials by optimizing accuracy and cost.
  • The findings support the advancement of high-performance lithium-ion battery technologies through data-driven material design.