AI与人类协作加速氧化钛/石墨烯外延生长实验设计
Human-AI collaborative autonomous synthesis with pulsed laser deposition for remote epitaxy
- 用大语言模型生成假设，协同策略驱动激光沉积实验
- 发现低氧低压低温可保石墨烯但不利氧化钛生长
- 提出两步氩氧沉积法，实现铁电层剥离与单层石墨烯保留
自主实验室通常依赖数据驱动决策，偶尔有人类介入提供领域知识。但要充分释放AI代理潜力，需紧密耦合的协作流程，涵盖假设生成、实验规划、执行与解读。为此，我们开发并部署了人机协作（HAIC）工作流，结合大语言模型进行假设生成与分析，通过协同策略更新实现自主脉冲激光沉积（PLD）实验，用于远程外延生长BaTiO₃/石墨烯。HAIC加速了假设形成与实验设计，高效映射了生长空间至石墨烯损伤区域。原位拉曼光谱显示，化学作用导致退化，而高能等离子体成分引发缺陷，识别出低氧压、低温合成窗口可保护石墨烯，但不适用于最优BaTiO₃生长。因此，我们提出两步氩/氧沉积法，在剥离铁电BaTiO₃的同时保持单层石墨烯界面。HAIC在自主批次间融合人类洞察与AI推理，推动快速科学进展，为众多现有‘人机协同’自主工作流提供演进路径。
原文摘要 · Abstract (English)
Autonomous laboratories typically rely on data-driven decision-making, occasionally with human-in-the-loop oversight to inject domain expertise. Fully leveraging AI agents, however, requires tightly coupled, collaborative workflows spanning hypothesis generation, experimental planning, execution, and interpretation. To address this, we develop and deploy a human-AI collaborative (HAIC) workflow that integrates large language models for hypothesis generation and analysis, with collaborative policy updates driving autonomous pulsed laser deposition (PLD) experiments for remote epitaxy of BaTiO$_3$/graphene. HAIC accelerated the hypothesis formation and experimental design and efficiently mapped the growth space to graphene-damage. In situ Raman spectroscopy reveals that chemistry drives degradation while the highest energy plume components seed defects, identifying a low-O$_2$ pressure low-temperature synthesis window that preserves graphene but is incompatible with optimal BaTiO$_3$ growth. Thus, we show a two-step Ar/O$_2$ deposition is required to exfoliate ferroelectric BaTiO$_3$ while maintaining a monolayer graphene interlayer. HAIC stages human insight with AI reasoning between autonomous batches to drive rapid scientific progress, providing an evolution to many existing human-in-the-loop autonomous workflows.
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