Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning. (arXiv:2310.03047v2 [physics.chem-ph] UPDATED)

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 …

arxiv battery deep learning designing differentiable electrolytes experimentation interfaces modeling next optimization physics robotic role systems transfer work