High energy batteries that are stable are sought by scientists for the electric grid. The inclusion of new sources of renewable energy such as solar and wind power into the electric grid will require large batteries specially designed for the purpose. These batteries will charge only when the sun is shining during the day and will release energy at night.
One type of battery is particularly useful for the purpose: The flow battery. Flow batteries comprise two tanks of electrically active chemicals with capability to exchange charge and can have large volumes to hold lot of energy.
The work on flow batteries involves finding target molecules that offers the ability to store a lot of energy as well as remain stable for long periods of time.
Meanwhile, to discover the right flow battery molecules researchers at the Argonne National Laboratory, U.S. Department of Energy have turned to the might of AI to search through a mega chemical space of more than a million molecules.
The discovery of right molecules requires optimizing among a number of different characteristics. It is known that a majority of molecules in these batteries that are required will have to satisfy multiple properties. The optimizing of several properties simultaneously indicates a better possibility to find the best possible chemistry for the battery.
In fact, nature is not perfect and not a single molecule is perfect in all ways. The model allows the ability to gauge the different parameters to identify the best fit.
The continuation of a research undertaken last year involved modeling anolyte redoxmers or electrically active molecules in a flow battery. The researchers found three properties for each redoxer that they wanted to optimize.