用AI迭代优化石墨负极，提升良品率和电池性能。
AI-Guided Design and Optimization of Graphite-Based Anodes via Iterative Experimental Feedback
- 通过AI反向设计，结合实验反馈逐步完善配方与工艺约束。
- 良品率从频繁失败升至100%，高容量电池比例从28.4%提至84.8%。
- 适合电池材料研发、智能制造领域的工程师与研究者。
本研究提出一种迭代式AI引导的工作流程，通过提升配方可行性与工艺鲁棒性，加速石墨基负极开发。利用Citrine平台实现基于AI/ML的多目标逆向设计，从噪声大、不完整的数据集出发，生成早期代理模型。尽管预测置信度低，但模型揭示了缺失的工艺约束。通过反复添加可行性标签与边界条件失效信息，工作流快速收敛至可量产、高性能的配方。制造可靠性从频繁失败提升至100%成功制备电池，容量≥350 mAh g⁻¹的电池占比从28.4%增至84.8%，容量保持率从42.1%提升至97.3%。结果表明，结构化、反馈驱动的AI流程能将不完美的工业数据转化为可操作指导，实现电池电极制造的更快、更可重复优化。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。