Designing the best molecular building blocks for battery components is like trying to create a recipe for a new kind of cake, when you have billions of potential ingredients.
Designing the best molecular building blocks for battery components is like trying to create a recipe for a new kind of cake, when you have billions of potential ingredients. The challenge involves determining which ingredients work best together — or, more simply, produce an edible (or, in the case of batteries, a safe) product. But even with state-of-the-art supercomputers, scientists cannot precisely model the chemical characteristics of every molecule that could prove to be the basis of a next-generation battery material.
Instead, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate the process of battery discovery.
As described in two new papers, Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes. To do so, they used a computationally intensive model called G4MP2. This collection of molecules, however, represented only a small subset of 166 billion larger molecules that scientists wanted to probe for electrolyte candidates.
Read more at DOE / Argonne National Laboratory
Image by Dean Simone from Pixabay
