Active Learning Guided Computational Discovery of Plant-Based Redoxmers for Organic Nonaqueous Redox Flow Batteries

Akash Jain, Ilya A. Shkrob, Hieu A. Doan, Keir Adams, Jeffrey S. Moore, Rajeev S. Assary

Research output: Contribution to journalArticlepeer-review


Organic nonaqueous redox flow batteries (O-NRFBs) are promising energy storage devices due to their scalability and reliance on sourceable materials. However, finding suitable redox-active organic molecules (redoxmers) for these batteries remains a challenge. Using plant-based compounds as precursors for these redoxmers can decrease their costs and environmental toxicity. In this computational study, flavonoid molecules have been examined as potential redoxmers for O-NRFBs. Flavone and isoflavone derivatives were selected as catholyte (positive charge carrier) and anolyte (negative charge carrier) molecules, respectively. To drive their redox potentials to the opposite extremes, in silico derivatization was performed using a novel algorithm to generate a library of > 40000 candidate molecules that penalizes overly complex structures. A multiobjective Bayesian optimization based active learning algorithm was then used to identify best redoxmer candidates in these search spaces. Our study provides methodologies for molecular design and optimization of natural scaffolds and highlights the need of incorporating expert chemistry awareness of the natural products and the basic rules of synthetic chemistry in machine learning.

Original languageEnglish (US)
Pages (from-to)58309-58319
Number of pages11
JournalACS Applied Materials and Interfaces
Issue number50
StatePublished - Dec 20 2023


  • Bayesian optimization
  • density functional theory
  • flavonoid
  • green chemistry
  • molecular libraries
  • nonaqueous redox flow battery
  • structure generation

ASJC Scopus subject areas

  • General Materials Science


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