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A molecular optimization framework to identify promising organic radicals for aqueous redox flow batteries

A molecular optimization framework to identify promising organic radicals for aqueous redox flow batteries

A computational strategy to find new structures optimized for organic redox flow batteries. Credit: Sondarya SV et al.

Recent advances in the development of machine learning and optimization techniques have opened up exciting new possibilities for identifying suitable molecular designs, compounds and chemical candidates for different applications. Optimization techniques, some of which are based on machine learning algorithms, are powerful tools that can be used to select optimal solutions for a given problem from a generally large set of possibilities.

Researchers from Colorado State University and the National Renewable Energy Laboratory have applied state-of-the-art molecular optimization models to different real-world problems that involve identifying new and promising molecular designs. In their most recent study, presented in Intelligence of natural machinesthey specifically applied a new open-source optimization framework to the task of identifying viable organic radicals for aqueous redox flow batteries, energy devices that convert chemical energy into electricity.

“Our project was funded by an ARPA-E program that sought to shorten the time needed to develop new energetic materials using machine learning techniques,” said Peter C. St. John, one of the researchers who conducted the study, at TechXplore. . “Finding new candidates for redox flow batteries was an interesting extension of some of our previous work, including a paper published in Nature Communication and another in Scientific databoth examining organic radicals.”

The new framework created by St. John and his colleagues was inspired by their previous work on molecular optimization. The framework essentially consists of the artificial intelligence (AI) tool AlphaZero, developed by DeepMind, coupled with a rapid model derived from machine learning, consisting of two graph neural networks trained on nearly 100,000 simulations of quantum chemistry.

The first of the graph’s neural networks was trained to predict oxidation and reduction potentials, two important parameters in determining how much energy can be stored in aqueous redox flow batteries. The second predicts the electron density and the local 3D environment, both of which have been shown to be associated with the lifetime of these batteries.

“We posit molecule optimization as a tree search, where we build molecules by adding components iteratively over a growing structure,” St. John explained. “The advantage of this approach is that we can prune large branches of the search space where molecules begin to show unrealistic substructures. We can therefore limit our search space to only molecules that meet a predetermined set simple criteria.

The researchers used their molecular optimization framework to perform a series of tests aimed at identifying possible organic radicals for aqueous redox flow batteries that might be particularly stable and promising. The framework successfully identified several molecular candidates that satisfied a specific combination of criteria defined by St. John and colleagues.

“We demonstrated that the set of possible candidates for a particular type of charge carrier in organic redox flow batteries may be larger than expected,” St. John said. “We have also shown that molecules can be found that could lead to simpler and better performing batteries without requiring the use of transition metals.”

So far, the optimization framework developed by this team of researchers has proven to be a very promising tool for solving complex real-world problems related to engineering and chemistry. In the future, it could thus be used to identify desirable new compounds and molecular candidates for many different technologies, including aqueous redox flow batteries.

“We would now like to explore adding additional criteria such as solubility and redox pairs between charged states,” St. John added. “This would require additional training data, but could lead to more promising candidate structures.”


A heteropolyacid negolyte that could improve the performance of low-temperature aqueous redox flow batteries


More information:
Shree Sowndarya SV et al, Targeted multi-objective optimization of de novo stable organic radicals for aqueous redox flow batteries, Intelligence of natural machines (2022). DOI: 10.1038/s42256-022-00506-3

Peter C. St. John et al, Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost, Nature Communication (2020). DOI: 10.1038/s41467-020-16201-z

Peter C. St. John et al, Quantum chemical calculations for over 200,000 species of organic radicals and 40,000 associated closed-shell molecules, Scientific data (2020). DOI: 10.1038/s41597-020-00588-x

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Quote: A Molecular Optimization Framework to Identify Promising Organic Radicals for Aqueous Redox Flow Batteries (September 15, 2022) Retrieved September 17, 2022 from https://techxplore.com/news/2022-09-molecular-optimization-framework -radicals-aqueous. html

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