RedDB, a computational database of electroactive molecules for aqueous redox flow batteries

Area of Science:

• Electrochemistry
• Materials Science
• Computational Chemistry

Background:

• High-performance redox flow batteries require novel electroactive compounds for grid-scale energy storage.
• Exploring the vast chemical space of potential compounds is challenging and time-consuming.
• High-throughput virtual screening is essential for efficient exploration of candidate molecules.

Purpose of the Study:

• To present RedDB, a computational database of organic electroactive compounds.
• To provide physicochemical properties for molecules relevant to redox flow batteries.
• To facilitate rapid screening and discovery of new battery materials.

Main Methods:

• Generation of a large virtual chemical library of quinones and aza-aromatics.
• Quantum chemical calculations for predicting molecular properties.
• Machine learning models for aqueous solubility prediction.
• Data processing and integration into a comprehensive database.

Main Results:

• Creation of RedDB, containing 31,618 organic molecules.
• Inclusion of diverse physicochemical properties relevant to battery performance.
• Demonstration of a systematic approach for database development.
• Establishment of a resource for accelerating materials discovery.

Conclusions:

• RedDB serves as a valuable resource for researchers in the field of redox flow batteries.
• The database enables faster identification of promising electroactive compounds.
• Computational approaches are crucial for advancing energy storage technologies.