RedPred: Redox Energy Prediction Tool for Redox Flow Battery Molecules
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RedPred is an reaction energy prediction model for redox flow battery molecules that consists consensus of 3 ML algorithms (Graph Conv Neural Nets, Random Forest, and Deep Neural Nets).
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You can upload or type your SMILES used as a reactant in the redox reaction to get the reaction energy (Hartree).
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RedPred is trained on RedDB [1] publicly available redox flow battery candidate molecules dataset.
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The performance of the RedPred is 0.0036 Hartree MAE on the test set.
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If you are using the predictions from RedPred on your work, please cite these papers: [1, 2]
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[1] Sorkun, Elif, et al. (2021). RedDB, a computational database of electroactive molecules for aqueous redox flow batteries.
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[2] In preparation (will be updated soon)
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Link for the web app: https://share.streamlit.io/mcsorkun/redpred-web/main/app.py
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2 (my_account) : https://share.streamlit.io/cihanyatbaz/redpred-web/main/app.py
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Murat Cihan Sorkun :
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Cihan Yatbaz :
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Elham Nour Ghassemi :
