All-solid-state batteries (ASSB) are widely viewed as a safer and potentially more energy-dense alternative to conventional lithium-ion batteries. Their performance relies heavily on how quickly ions can move through solid electrolytes. Finding materials that enable this rapid ion transport has traditionally required extensive synthesis and experimental testing. Researchers also rely on computer simulations, but many existing computational approaches struggle to accurately represent the disordered and high-temperature conditions where ions move most freely.
Predicting when ions will move through a solid in a liquid-like way has been especially difficult. Standard computational methods that simulate these complex systems demand enormous computing resources, making them impractical for screening large numbers of candidate materials.
To overcome these challenges, researchers developed a machine learning (ML) accelerated workflow that combines ML force fields with tensorial ML models to simulate Raman spectra. Their results show that strong low-frequency Raman intensity can serve as a clear spectroscopic marker of liquid-like ion conduction.
To read more, click here.