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Achieving a fully renewable energy future requires the development of grid-scale energy storage technologies, and non-aqueous redox flow batteries (NRFBs) hold considerable potential. 1 However, the redox-active materials (RAMs) currently in consideration as lead candidates for NRFBs exhibit shortcomings, including low solubility, impaired transport properties, and low reduction potentials. Utilizing theory and computation is invaluable in predicting the structure-property relationship of RAM as NRFB active materials. The numerous components within NRFB electrolytes, spanning solvents, active materials, and supporting salts, suggest that material discovery could experience a substantial acceleration through computational and machine-learning approaches. This presentation will provide a progress update on our efforts towards a computational framework involving quantum mechanical (QM), molecular dynamics (MD), and machine learning methods to predict fundamental chemical and physicochemical properties of redox-active materials for energy storage systems. We used an extremely stable, bio-inspired vanadium(4+) bis-hydroxyiminodiacetic acid (VBH) as the scaffold to explore the large parameter space of NRFB systems. 2–4 First, we show our framework based on density functional theory (DFT) for calculating solubilities, incorporating both lattice and solvation energies, that can be calibrated and used with synthetic verifications. We found that lattice free energy, which has previously been neglected, has a significant role in tuning electrolyte solubility. Second, we describe our MD simulations on the concentration and composition dependence of transport properties, such as viscosity, using DFT-derived force field bonded parameters for the novel VBH 2- anion to understand the interactions between the ions and the solvent. Third, we demonstrate that QM molecular design could finely adjust the reduction potential. These fundamental investigations would offer insights into electrolyte design and how molecular properties dictate the macroscopic properties that contribute to specific performance aspects of advanced battery electrolytes. 1 Y. Huang, S. Gu, Y. Yan and S. F. Y. Li, Current Opinion in Chemical Engineering , 2015, 8 , 105–113. 2 H. Huang, R. Howland, E. Agar, M. Nourani, J. A. Golen and P. J. Cappillino, J. Mater. Chem. A , 2017, 5 , 11586–11591. 3 S. K. Pahari, T. C. Gokoglan, B. R. B. Visayas, J. Woehl, J. A. Golen, R. Howland, M. L. Mayes, E. Agar and P. J. Cappillino, RSC Adv. , 2021, 11 , 5432–5443. 4 B. R. B. Visayas, S. K. Pahari, T. C. Gokoglan, J. A. Golen, E. Agar, P. J. Cappillino and M. L. Mayes, Chem. Sci. , 2021, 12 , 15892–15907.
Mayes et al. (Fri,) studied this question.