AI-Guided Design and Optimization of Graphite-Based Anodes via Iterative Experimental Feedback
For battery electrode manufacturing, this work demonstrates that structured AI feedback can transform noisy industrial data into actionable guidance, enabling faster and more reproducible optimization.
This study presents an AI-guided iterative workflow for optimizing graphite-based anodes, improving fabrication reliability from frequent failures to 100% success, increasing the fraction of cells delivering ≥350 mAh g⁻¹ from 28.4% to 84.8%, and boosting capacity retention from 42.1% to 97.3%.
This study presents an iterative AI-guided workflow that accelerates graphite-based anode development by improving both formulation feasibility and process robustness. Sequential learning via AI/ML-guided multiobjective inverse design for anode optimization was implemented using the Citrine Platform. Starting from a noisy, incomplete dataset, the Citrine Platform was used to generate early surrogate models, which despite low predictive certainty highlighted missing process constraints. By iteratively adding feasibility labels and boundary condition failures, the workflow rapidly converged toward manufacturable, higher-performing formulations. Fabrication reliability improved from frequent process failures to 100% successful cell production, while the fraction of cells delivering $\geq$ 350 mAh g$^{-1}$ increased from 28.4% to 84.8%, with capacity retention rising from 42.1% to 97.3%. These results demonstrate that structured, feedback-driven AI workflows can transform imperfect industrial data into actionable guidance, enabling faster, more reproducible optimization of battery electrode manufacturing.