High throughput selection of organic cathode materials

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Traditional inorganic battery materials face limitations in efficiency and cost.
• Novel organic electrode materials offer a promising alternative for advanced battery designs.
• Computational screening is crucial for identifying viable organic candidates efficiently.

Purpose of the Study:

• To computationally screen a vast database of organic molecules for potential use as battery electrode materials.
• To identify candidates with high electrochemical performance, including reduction potential and energy density.
• To provide a curated list of promising organic molecules for experimental validation.

Main Methods:

• Density Functional Theory (DFT) high-throughput computational screening of 86 million molecules from the PubChem database.
• Analysis and classification of molecules based on predicted electrochemical properties.
• Identification of top-performing candidates exceeding specific energy density and voltage thresholds.

Main Results:

• 5,445 top-performing organic candidates were identified.
• 2,306 candidates predicted to have a one-electron reduction potential > 4 V vs. Li/Li+.
• 626 molecules predicted with one-electron energy densities > 800 Wh kg⁻¹; 69 with two-electron energy densities > 1300 Wh kg⁻¹.
• Identified molecules belong to various families, including novel and established organic electrode material types, often featuring conjugated C-O, N-N, or N-C groups.

Conclusions:

• The computational screening successfully identified a significant number of high-performance organic molecules for battery electrodes.
• The study provides a valuable resource for researchers seeking new materials for efficient and affordable energy storage.
• The findings pave the way for accelerated experimental development of next-generation organic batteries.