Paper

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

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arXiv:2606. 00187v1 Announce Type: new Abstract: This study presents an iterative AI-guided workflow that accelerates graphite-based anode development by improving both formulation feasibility and process robustness.

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Authors: Qian Du, Mark M. Sullivan, James E. Saal, Florian Huber
Released: 2026-06-02 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because AI-Guided Design and Optimization of Graphite-Based Anodes via Iterative Experimental Feedback overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: rate, implement, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.00187

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