Synthesis-Driven Computational Discovery of Organic Redoxmers for Non-Aqueous Redox Flow Batteries

Organic non-aqueous redox flow batteries (RFBs) are promising grid-scale energy storage systems for storing intermittent renewable energy in molecules. For practical applications, low-cost and stable redox active molecules (redoxmers) that display a large redox potential window and long electrochemical cycling stability are required to deliver a high-energy-density RFB with a long battery cycle life. To accelerate the discovery of redoxmer molecules, previous studies have utilized machine learning (ML) methods along with first-principles simulations. Although many ML-suggested molecules show promising electrochemical properties, they are generally difficult to synthesize because of their highly complex structure. To address this challenge, in this work, we propose a new synthesis-driven computational approach to discovering less-complex molecules with a large redox potential window that can deliver high-energy-density RFBs. Specifically, we devised an algorithm to generate an engineered molecular search space and applied a Bayesian optimization-based active learning algorithm to discover promising molecules from a large molecular search space while using a limited number of expensive density functional theory (DFT) calculations. To increase the energy density of RFBs, we focus on maximizing the redox potential window of redoxmer molecules (or cell voltage) by minimizing the reduction potential of anolyte molecules. Precisely, we studied the 2,1,3 Benzothiadiazole (BTZ) molecule, a promising anolyte (redoxmer) candidate for RFBs, and with our synthesis-driven approach discovered new BTZ-based molecules that show 15-43% smaller reduction potential than the BTZ and in principle can deliver organic RFBs with a cell voltage ≈ 3 V.