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Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening.

Lei Cheng¹, Rajeev S Assary¹, Xiaohui Qu²

¹†Materials Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States.

The Journal of Physical Chemistry Letters

|August 12, 2015

Summary

This study introduces a computational screening method to discover new electrolytes for advanced electrical energy storage systems. The approach efficiently identifies promising candidate molecules, accelerating materials discovery for future energy needs.

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Area of Science:

Materials Science
Computational Chemistry
Electrochemistry

Background:

Traditional experimental methods for discovering new materials are time-consuming and costly.
Advanced electrical energy storage systems require novel electrolytes for improved performance.
Computational screening offers an efficient alternative to experimental trial-and-error.

Purpose of the Study:

To review electrolyte screening methods.
To present a hierarchical computational scheme for high-throughput screening of advanced electrical energy storage electrolytes.
To demonstrate the in silico design of candidate molecules for synthesis and testing.

Main Methods:

Utilized high-throughput quantum chemical calculations for screening multiple electrolyte properties.
Employed a hierarchical approach to successively evaluate properties and down-select candidates.
Screened approximately 1400 organic molecules for nonaqueous redox flow batteries.

Main Results:

Successfully screened a large pool of organic molecules based on redox potentials, solvation energies, and structural changes.
Demonstrated the effectiveness of the computational scheme in identifying suitable electrolyte candidates.
Provided a pathway for in silico design of molecules for synthesis and electrochemical testing.

Conclusions:

Computational screening is a powerful tool for accelerating the discovery of new materials for electrical energy storage.
The described hierarchical computational scheme enables efficient identification of promising electrolyte candidates.
Integrating computational approaches with experimentation is crucial for meeting future energy demands.