AI-Guided Design and Optimization of Graphite-Based Anodes via Iterative Experimental Feedback

2026-05-29

AI Summary

This study addresses the longstanding challenges in developing conventional graphite anodes—namely low formulation feasibility, poor process robustness, and noisy, incomplete experimental data—which collectively result in prolonged development cycles and low yield. To overcome these limitations, this work proposes an AI/ML-driven multi-objective inverse design workflow built on the Citrine platform, featuring an innovative structured feedback mechanism. By leveraging sequential learning and surrogate modeling under data-scarce conditions, the approach effectively learns from failed experiments to rapidly delineate process constraint boundaries. The resulting closed-loop optimization strategy dramatically enhances development efficiency: battery fabrication success rate reaches 100%, the fraction of cells achieving capacity ≥350 mAh g⁻¹ increases from 28.4% to 84.8%, and capacity retention improves from 42.1% to 97.3%.

Abstract

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.

Problem

• graphite-based anodes
• formulation feasibility
• process robustness
• battery electrode manufacturing
• multiobjective optimization

Innovation

• AI-guided design
• iterative feedback
• multiobjective inverse design
• surrogate modeling
• battery anode optimization