Data‐Driven Virtual Material Analysis and Synthesis for Solid Electrolyte Interphases

Solid electrolyte interphases (SEIs) form as reduction products at the electrodes and strongly affect battery performance and safety. Because SEI formation poses a highly nonlinear, complex multi-physics problem over various lengths and time scales, traditional modeling approaches struggle to characterize SEI evolution solely with existing physical properties. To improve the characterization of SEIs, it proposes a data-driven strategy for a virtual material design that learns to represent and characterize SEI formation with physical and data-driven properties from kinetic Monte Carlo simulations. A Variational AutoEncoder with a property regressor learns data-driven properties, which represent SEI configurations and correlate with physical target properties. This new neural network design encodes the high-dimensional structural and reaction spaces into a lower-dimensional latent space, while the property regressor orders the latent space by physical target properties. The model achieves high correlation scores between target and predicted properties from latent representations, thereby proving that the data-driven properties enrich the expressiveness of SEI characterizations.