RedDB

RedDB provides a curated database of 31,618 quinones and aza-aromatics with computed physicochemical properties for evaluation and high-throughput screening of electroactive compounds for aqueous redox flow batteries and grid-scale energy storage.

Key Features:

• Chemical Library Generation: Systematic combinatorial enumeration of quinones and aza-aromatics to produce a large virtual chemical library.
• Molecule Count: Contains 31,618 curated organic electroactive molecules from the quinone and aza-aromatic classes.
• Molecular Property Prediction: Quantum chemical calculations used to predict electronic and other molecular properties relevant to battery performance.
• Aqueous Solubility Prediction: Machine learning models applied to predict aqueous solubility of compounds.
• Physicochemical Descriptors: Comprehensive physicochemical property data provided as potential descriptors for assessing compound suitability in redox flow batteries.
• Data Processing and Database Creation: Processed and organized computational results consolidated into a searchable database format.
• High-throughput Virtual Screening Support: Compiled computed properties enable large-scale virtual screening of electroactive candidates.

Scientific Applications:

• Redox Flow Battery Materials Discovery: Identification and prioritization of quinones and aza-aromatics for aqueous redox flow batteries and grid-scale energy storage.
• Performance Evaluation: Use of computed electronic and physicochemical descriptors to assess expected battery-relevant properties.
• Solubility Assessment: Prediction of aqueous solubility to evaluate practical applicability of candidates in aqueous flow systems.
• High-throughput Screening and Optimization: Systematic exploration and optimization of organic electroactive molecules via large-scale virtual screening.

Methodology:

Combinatorial chemical library generation of quinones and aza-aromatics, quantum chemical calculations for molecular property prediction, machine learning models for aqueous solubility prediction, and processing/organization of results into a database.