@article{9033,
  author = {M.C. Sorkun and E.N. Ghassemi and C. Yatbaz and J.M.V.A. Koelman and S. Er},
  title = {RedPred, a machine learning model for the prediction of redox reaction energies of the aqueous organic electrolytes},
  abstract = {Aqueous Organic Redox Flow Batteries (AORFBs) are considered as one of the most appealing technologies for large-scale energy storage due to their electroactive organic materials, which are abundant, easy to produce, and recyclable. A prevailing challenge for the redox chemistries applied in AORFBs is to achieve high power and energy density. The chemical design and molecular engineering of the electroactive compounds is an effective approach for the optimization of their physicochemical properties. Among them, the reaction energy of redox couples is often used as a proxy for the measured potentials. In this study, we present RedPred, a machine learning (ML) model that predicts the one-step two-electron two-proton redox reaction energy of redox-active molecule pairs. RedPred comprises an ensemble of Artificial Neural Networks, Random Forests, and Graph Convolutional Networks, trained using the RedDB database, which contains over 15,000 reactant-product pairs for AORFBs. We evaluated RedPred’s performance using six different molecular encoders and five prominent ML algorithms applied in chemical science. The predictive capability of RedPred was tested on both its training chemical space and the chemical space outside its training domain using two separate test datasets. We released a user-friendly web tool with open-source code to promote software sustainability and broad use.},
  year = {2024},
  journal = {Artificial Intelligence Chemistry},
  volume = {2},
  pages = {100064},
  month = {06/2024},
  doi = {10.1016/j.aichem.2024.100064},
  language = {eng},
}