RedCat, an automated discovery workflow for aqueous organic electrolytes

Abstract

Developing cost-effective organic molecules with robust redox activity and high solubility is crucial for widespread acceptance and deployment of aqueous organic redox flow batteries (AORFBs). We present RedCat, an automated workflow designed to accelerate the discovery of redox-active organic molecules from extensive molecular databases. This workflow employs structure-based selection, machine learning models for predicting redox reaction energy and aqueous solubility, and dynamically integrates up-to-date pricing data to prioritize candidates. Applying this workflow to 112 million molecules from the PubChem database, we identified 261 promising anolyte candidates. We validated their battery-related properties through first-principles and molecular dynamics calculations and experimentally tested two electrochemically active molecules. These molecules demonstrated higher energy densities than previously reported compounds, confirming the robustness of our workflow in discovering electrolytes. With its open-access code repository and modular design, RedCat is well-suited for integration into self-driving labs, offering a scalable framework for autonomous, data-driven electrolyte discovery.