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

Shang Zhu, Bharath Ramsundar, Emil Annevelink, Hongyi Lin, Adarsh Dave, Pin-Wen Guan, Kevin Gering and Venkatasubramanian Viswanathan ()
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Shang Zhu: Carnegie Mellon University
Bharath Ramsundar: Deep Forest Sciences
Emil Annevelink: Carnegie Mellon University
Hongyi Lin: Carnegie Mellon University
Adarsh Dave: Carnegie Mellon University
Pin-Wen Guan: Carnegie Mellon University
Kevin Gering: Idaho National Laboratory
Venkatasubramanian Viswanathan: Carnegie Mellon University

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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 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.

Date: 2024
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DOI: 10.1038/s41467-024-51653-7

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