DIFFER database maps 31,618 molecules with potential for energy storage
Researchers at the Dutch Institute for Fundamental Energy Research (DIFFER) have created a database named 'RedDB' identifying 31,618 molecules that could potentially be used in future redox-flow batteries that hold great promise for energy storage.
The findings of the research has been published in the journal Scientific Data. Among other things, the researchers have used artificial intelligence and supercomputers to identify the molecules' properties.
In recent times, the scientific community has designed hundreds of molecules that could potentially be useful in flow batteries for energy storage. It would have be wonderful if the properties of these molecules were quickly and easily accessible in a database.
The problem, however, is that for many molecules the properties are not known. Examples of molecular properties are redox potential and water solubility. Those are important since they are related to the power generation capability and energy density of redox flow batteries.
To overcome this limitation, the researchers used smart algorithms to create thousands of virtual variants of two types of molecules. These molecule families, the quinones and aza aromatics, are good at reversibly accepting and donating electrons. The researchers then fed the computer with backbone structures of 24 quinones and 28 aza-aromatics plus five different chemically relevant side groups. From that, the computer created 31,618 different molecules.
Next, the DIFFER researchers used supercomputers to calculate nearly 300 different properties of each molecule, using equations from quantum chemistry. The supercomputers came in handy to solve difficult formulae. Further, machine learning was used to predict whether the molecules would be dissolvable in water.
Finally, human- and machine-readable database called RedDB (from Redox DataBase) containing the molecules and their properties with convenient naming and description was created.
"When you work with theoretical models and machine learning, you obviously want to be confident in the results," says Süleyman Er, the leader of DIFFER's Autonomous Energy Materials Discovery research group. "This is why we used computer programs that have proven their excellence. For this purpose, we also implemented dedicated validation procedures."
Now that the database is public, global researchers can easily search for potentially interesting molecules for redox flow batteries. For instance, they can simply purchase or synthesize the molecules and research them further.
Moreover, the researchers may use the database to improve their machine-learning models to speed up the design of high-quality molecules for energy storage, according to the DIFFER.
