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A reduction-oxidation reaction is commonly called a redox reaction. In a redox reaction, electrons are transferred from one species to another rather than being shared between or among atoms. The reducing agent or reductant is the species that loses electrons and gets oxidized in the process. The species that gains electrons and gets reduced in the process is the oxidizing agent or oxidant. Redox reactions are represented as two separate equations called half-reactions, where one equation...
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Ladder diagrams are useful tools for understanding redox equilibrium reactions, especially the effects of concentration changes on the electrochemical potential of the reaction. The vertical axis in the redox ladder diagrams represents the electrochemical potential, E. The area of predominance is demarcated using the Nernst equation.
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SOMAS: a platform for data-driven material discovery in redox flow battery development.

Peiyuan Gao1, Amity Andersen2, Jonathan Sepulveda3

  • 1Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA. peiyuan.gao@pnnl.gov.

Scientific Data
|December 1, 2022
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Summary
This summary is machine-generated.

A new database, SOMAS, offers over 12,000 organic molecules to improve machine learning models for predicting aqueous solubility, crucial for developing safer, large-scale energy storage solutions like aqueous organic redox flow batteries.

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Area of Science:

  • Chemistry
  • Materials Science
  • Energy Storage

Background:

  • Aqueous organic redox flow batteries are promising for grid-scale energy storage due to their safety and tunability.
  • High energy density in these batteries relies on the aqueous solubility of redox-active organic molecules.
  • Predicting molecular solubility in water is a significant challenge in chemistry.

Purpose of the Study:

  • To create a comprehensive, open-access database of organic molecules and their aqueous solubility.
  • To provide essential data for developing accurate machine learning models for solubility prediction.
  • To support advancements in aqueous organic redox flow battery technology.

Main Methods:

  • Compiled a database (SOMAS) of approximately 12,000 organic molecules.
  • Included experimental solubility data for each molecule.
  • Generated quantum and molecular descriptors using high-throughput density functional theory calculations.

Main Results:

  • Established the "Solubility of Organic Molecules in Aqueous Solution" (SOMAS) database.
  • SOMAS contains diverse molecules covering wide chemical and solubility ranges.
  • The database includes experimental solubility, optimized geometries, and 14 molecular descriptors per molecule.

Conclusions:

  • The SOMAS database provides a critical foundation for AI-driven solubility prediction.
  • This resource will accelerate the development of effective machine learning models for organic redox flow batteries.
  • Facilitates improved design of molecules for efficient large-scale aqueous energy storage.