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<p>An interactive web tool, <b>PredPotS</b>, has been developed for predicting one-electron standard reduction potentials of organic molecules in aqueous solutions. The predictions are generated using deep learning models trained and validated on a chemically diverse database comprising reduction potentials of approximately 8000 organic compounds. The reduction potentials of this database were computed using a composite computational protocol that combines the semiempirical quantum chemical method (GFN2-xTB) and a well-established DFT approach (M06-2X functional along with the SMD solvent model). While this computational approach is cost-effective, it is subject to certain limitations, which are nonetheless duly accounted for in the development of the database. The applied graph-based deep learning methods perform remarkably well in terms of the standard performance metrics. By entering or uploading the SMILES codes of the molecules, <b>PredPotS</b> provides fast and sensible predictions for one-electron standard reduction potentials for a diverse set of organic molecules also in the range compatible with the electrochemical stability of aqueous electrolytes. The <b>PredPotS</b> web tool is particularly well-suited for screening redox-active candidates for aqueous organic redox flow batteries, but it may also prove useful in a variety of other electrochemical applications.</p>

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PredPotS: web tool for predicting one-electron standard reduction potentials for organic molecules in aqueous phase

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月7日

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  • Flóra B. Németh,
  • Andrea Hamza,
  • Beatrix Tugyi,
  • Maya El-Ali,
  • Luca Szegletes,
  • Ádám Madarász,
  • Imre Pápai

摘要

An interactive web tool, PredPotS, has been developed for predicting one-electron standard reduction potentials of organic molecules in aqueous solutions. The predictions are generated using deep learning models trained and validated on a chemically diverse database comprising reduction potentials of approximately 8000 organic compounds. The reduction potentials of this database were computed using a composite computational protocol that combines the semiempirical quantum chemical method (GFN2-xTB) and a well-established DFT approach (M06-2X functional along with the SMD solvent model). While this computational approach is cost-effective, it is subject to certain limitations, which are nonetheless duly accounted for in the development of the database. The applied graph-based deep learning methods perform remarkably well in terms of the standard performance metrics. By entering or uploading the SMILES codes of the molecules, PredPotS provides fast and sensible predictions for one-electron standard reduction potentials for a diverse set of organic molecules also in the range compatible with the electrochemical stability of aqueous electrolytes. The PredPotS web tool is particularly well-suited for screening redox-active candidates for aqueous organic redox flow batteries, but it may also prove useful in a variety of other electrochemical applications.