Why we write about this topic:
Batteries are used for everything. Because of the energy transition, the demand for better batteries has grown. Not only should they be able to relieve the power grid during congestion, they also have to power electric cars and planes. With this database, the super battery is another step closer. That is why Innovation Origins selected this post.
Scientists from the Dutch Institute for Fundamental Energy Research (DIFFER) have created a public database of 31.618 molecules that could potentially be used in future batteries. The researchers used artificial intelligence and supercomputers to identify the molecules’ properties, writes the research institute in a press release.
In recent years, chemists have designed hundreds of molecules that could potentially be useful in flow batteries for energy storage. The problem, however, is that for many molecules the properties are not known. That’s why DIFFER created a database, for which they used smart algorithms and a supercomputer.
Now that the database is public, researchers, including those outside DIFFER, 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.
Making a database in four steps
To find out the still-unknown properties of molecules, the researchers performed four steps. First, they used a desktop computer and smart algorithms to create thousands of virtual variants of two types of molecules and five different chemically relevant side groups. From that, the computer created 31,618 different molecules.
In the second step, the researchers used supercomputers to calculate nearly 300 different properties of each molecule. The computer uses equations from quantum chemistry to do this. Because these formulas are difficult to solve, a powerful supercomputer is handy in use.
In the third step, the researchers used machine learning to predict whether the molecules would be dissolvable in water. The fourth and final step consisted of creating a both human- and machine-readable database. The database, called RedDB (from Redox DataBase), contains the molecules and their properties with convenient naming and description.
The researchers published their findings in the journal Scientific Data.
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