Advances in the field of goal-directed molecular optimization offer the promise of finding feasible candidates for even the most challenging molecular design applications. One example of a fundamental design challenge is the search for novel stable radical scaffolds for an aqueous redox flow battery that simultaneously satisfy redox requirements at the anode and cathode, as relatively few stable organic radicals are known to exist. To meet this challenge, we develop a new open-source molecular optimization framework based on AlphaZero coupled with a fast, machine-learning-derived surrogate objective trained with nearly 100,000 quantum chemistry simulations. The objective function comprises two graph neural networks: one that predicts adiabatic oxidation and reduction potentials and a second that predicts electron density and local three-dimensional environment, previously shown to be correlated with radical persistence and stability. With no hard-coded knowledge of organic chemistry, the reinforcement learning agent finds molecule candidates that satisfy a precise combination of redox, stability and synthesizability requirements defined at the quantum chemistry level, many of which have reasonable predicted retrosynthetic pathways. The optimized molecules show that alternative stable radical scaffolds may offer a unique profile of stability and redox potentials to enable low-cost symmetric aqueous redox flow batteries.
S. V., Shree Sowndarya, et al. "Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries." Nature Machine Intelligence, vol. 4, no. 8, Aug. 2022. https://doi.org/10.1038/s42256-022-00506-3
S. V., Shree Sowndarya, Law, Jeffrey N., Tripp, Charles E., Duplyakin, Dmitry, Skordilis, Erotokritos, Biagioni, David, Paton, Robert S., & St. John, Peter C. (2022). Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries. Nature Machine Intelligence, 4(8). https://doi.org/10.1038/s42256-022-00506-3
S. V., Shree Sowndarya, Law, Jeffrey N., Tripp, Charles E., et al., "Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries," Nature Machine Intelligence 4, no. 8 (2022), https://doi.org/10.1038/s42256-022-00506-3
@article{osti_1879783,
author = {S. V., Shree Sowndarya and Law, Jeffrey N. and Tripp, Charles E. and Duplyakin, Dmitry and Skordilis, Erotokritos and Biagioni, David and Paton, Robert S. and St. John, Peter C.},
title = {Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries},
annote = {Abstract Advances in the field of goal-directed molecular optimization offer the promise of finding feasible candidates for even the most challenging molecular design applications. One example of a fundamental design challenge is the search for novel stable radical scaffolds for an aqueous redox flow battery that simultaneously satisfy redox requirements at the anode and cathode, as relatively few stable organic radicals are known to exist. To meet this challenge, we develop a new open-source molecular optimization framework based on AlphaZero coupled with a fast, machine-learning-derived surrogate objective trained with nearly 100,000 quantum chemistry simulations. The objective function comprises two graph neural networks: one that predicts adiabatic oxidation and reduction potentials and a second that predicts electron density and local three-dimensional environment, previously shown to be correlated with radical persistence and stability. With no hard-coded knowledge of organic chemistry, the reinforcement learning agent finds molecule candidates that satisfy a precise combination of redox, stability and synthesizability requirements defined at the quantum chemistry level, many of which have reasonable predicted retrosynthetic pathways. The optimized molecules show that alternative stable radical scaffolds may offer a unique profile of stability and redox potentials to enable low-cost symmetric aqueous redox flow batteries.},
doi = {10.1038/s42256-022-00506-3},
url = {https://www.osti.gov/biblio/1879783},
journal = {Nature Machine Intelligence},
issn = {ISSN 2522-5839},
number = {8},
volume = {4},
place = {United Kingdom},
publisher = {Nature Publishing Group},
year = {2022},
month = {08}}