  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 conductivity of
electrolytes by over 18.8% within 10 experimental steps, via differentiable
optimization built on DiffMix gradients. By combining GDL, mixture physics
laws, and robotic experimentation, DiffMix expands the predictive modeling
methods for chemical mixtures and enables efficient optimization in large
chemical spaces.
