Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning.

ABSTRACT: Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic c

SUBMITTER: Zhu S 

PROVIDER: S-EPMC11455955 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature