ArticleScientific data2022
RedDB, a computational database of electroactive molecules for aqueous redox flow batteries.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Computational Evaluation of Redox Potentials of Metal Complexes for Aqueous Flow Batteries.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Article
- Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning.Scientific data · 2024Article
- Llamol: a dynamic multi-conditional generative transformer for de novo molecular design.Journal of cheminformatics · 2024Article
- Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design.Journal of chemical theory and computation · 2023Article
- Benchmarking organic active materials for aqueous redox flow batteries in terms of lifetime and cost.Nature communications · 2023Review
- Density functional theory and machine learning for electrochemical square-scheme prediction: an application to quinone-type molecules relevant to redox flow batteries.Digital discovery · 2023Article
- Discovery of lead quinone cathode materials for Li-ion batteries.Digital discovery · 2023Article
- Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks.Journal of chemical theory and computation · 2023Review
- Prediction of Redox Power for Photocatalysts: Synergistic Combination of DFT and Machine Learning.Journal of chemical theory and computation · 2023Article
- Virtual screening of organic quinones as cathode materials for sodium-ion batteries.Energy advances · 2023Article
- RedDB, a computational database of electroactive molecules for aqueous redox flow batteries.Scientific data · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
An increasing number of electroactive compounds have recently been explored for their use in high-performance redox flow batteries for grid-scale energy storage. Given the vast and highly diverse chemical space of the candidate compounds, it is alluring to access their physicochemical properties in a speedy way. High-throughput virtual screening approaches, which use powerful combinatorial techniques for systematic enumerations of large virtual chemical libraries and respective property evaluations, are indispensable tools for an agile exploration of the designated chemical space. Herein, RedDB: a computational database that contains 31,618 molecules from two prominent classes of organic electroactive compounds, quinones and aza-aromatics, has been presented. RedDB incorporates miscellaneous physicochemical property information of the compounds that can potentially be employed as battery performance descriptors. RedDB's development steps, including: (i) chemical library generation, (ii) molecular property prediction based on quantum chemical calculations, (iii) aqueous solubility prediction using machine learning, and (iv) data processing and database creation, have been described.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.