As the clean transition drives uptake of electric vehicles and energy storage for an electricity grid with ever greater dependence on variable renewable energy sources such as wind and solar, the danger from battery fires grows as well. To limit this risk while improving battery performance, the next generation of batteries is likely to depend on new solid-state electrolytes, but research has been hampered by the sheer volume of material options and the parameters involved.
Machine learning, however, is coming to the rescue. A group of materials scientists have developed a new, dynamic database of hundreds of solid-state electrolytes to which they have applied artificial intelligence techniques that are already steering research in better directions.
A paper describing their approach was published in the journal Nano Materials Science on September 10, 2023.
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