AI 中文总结
该研究提出一种结合LLM、高光谱成像与多目标贝叶斯优化的闭环框架，可自动化材料合成工作流程，在配对优化中表现优于基线方法，还成功合成了未报道的钙钛矿型化合物Rb3BiI6薄膜。
AI 中文摘要
大多数计算预测的材料从未被合成，因为传统的合成优化速度慢、依赖专业知识且具有迭代性。在此，我们提出一种闭环框架，该框架通过将人类隐性知识融入循环来自动化这一专家工作流程，具体包括：从文献中提炼合成知识的大语言模型（LLM）、用于快速薄膜评估的高通量高光谱成像，以及由实验反馈引导的多目标贝叶斯优化。在配对优化实验中，在匹配的试验次数下，LLM辅助初始化产生的帕累托最优样本更多，超体积也高于拉丁超立方采样基线，且该优势在整个迭代优化过程中持续存在。我们通过合成此前未报道的钙钛矿型化合物Rb3BiI6作为薄膜，并通过光学带隙分析和X射线衍射验证优化后的薄膜，来演示该框架。该框架将合成预测从单次推荐转变为迭代学习，为加速自动化及完全自主的实验材料发现提供了可推广策略。
英文摘要
Most computationally predicted materials are never synthesized because conventional synthesis optimization is slow, expertise-dependent, and iterative. Here we present a closed-loop framework that automates this expert workflow by placing human tacit knowledge in the loop through a large language model (LLM) that distills synthesis knowledge from the literature, high-throughput hyperspectral imaging for rapid film evaluation, and multi-objective Bayesian optimization guided by experimental feedback. In a paired optimization campaign, LLM-assisted initialization produced more Pareto-optimal samples and higher hypervolume than a Latin hypercube sampling baseline at matched trial counts, and this advantage persisted throughout iterative optimization. We demonstrate the framework by synthesizing the previously unreported perovskite-inspired compound Rb3BiI6 as thin films and validating the optimized films by optical bandgap analysis and X-ray diffraction. The framework transforms synthesis prediction from single-shot recommendation to iterative learning, providing a generalizable strategy to accelerate automated and fully autonomous experimental materials discovery.
Comments38 pages, 5 figures
