Zhu Shang, Ramsundar Bharath, Annevelink Emil, Lin Hongyi, Dave Adarsh, Guan Pin-Wen, Gering Kevin, Viswanathan Venkatasubramanian
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, USA.
Department of Mechanical Engineering, University of Michigan, Ann Arbor, USA.
Nat Commun. 2024 Oct 5;15(1):8649. doi: 10.1038/s41467-024-51653-7.
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.
电解质在设计下一代电池系统中起着关键作用，它能实现高效的离子转移、防止电荷转移并稳定电极-电解质界面。在这项工作中，我们开发了一种用于化学混合物的可微几何深度学习（GDL）模型DiffMix，该模型用于指导机器人实验以及针对快速充电电池电解质的优化。具体而言，我们通过创建可由GDL学习的物理系数来扩展混合物热力学和传输定律。我们用混合物热力学和离子传输特性评估了我们的模型，结果表明DiffMix比其纯数据驱动的变体具有更高的预测准确性和模型鲁棒性。此外，通过机器人实验装置Clio，基于DiffMix梯度进行可微优化，我们在10个实验步骤内将电解质的离子电导率提高了超过18.8%。通过结合GDL、混合物物理定律和机器人实验，DiffMix扩展了化学混合物的预测建模方法，并能在大型化学空间中实现高效优化。
