Narrowing down 166 billion battery materials

Researchers at the U.S. Argonne National Laboratory have applied a combination of machine learning and artificial intelligence to help narrow down a list of 166 billion molecules that could be used to form the basis of a battery electrolyte. The technique, say the researchers, offers a way to greatly reduce the cost of narrowing down such an enormous data set, while still providing a precise understanding of each molecule and its likely suitability.The search for new materials and battery chemistries is an important one, as scientists look for materials that can match or exceed the performance ...

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