AI RESEARCH
AI-Guided Design and Optimization of Graphite-Based Anodes via Iterative Experimental Feedback
arXiv CS.LG
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ArXi:2606.00187v1 Announce Type: new 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.