PredPotS: web tool for predicting one-electron standard reduction potentials for organic molecules in aqueous phase
Source abstract
Abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced battery technologies research · Machine Learning in Materials Science · Electrocatalysts for Energy Conversion
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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