基于多模态LLM的自主材料合成框架SynAgent发布
Hypothesis-Driven Autonomous Materials Synthesis with Multimodal LLM Agents
这个SynAgent框架挺有意思，用LLM当实验员，自己探索条件，还自己分析数据，最后能总结出规律，比传统黑箱优化器强。
SynAgent框架让大语言模型操作自动化实验系统，在18次独立实验中成功合成出高结晶度薄膜，并发现衬底温度在650-690°C时存在结晶临界点和最佳生长窗口，将自主实验从优化样本扩展到可验证、可读的理解。
Hypothesis-Driven Autonomous Materials Synthesis with Multimodal LLM Agents
Self-driving laboratories can explore synthesis conditions autonomously, but their decision-making layer is typically a black-box optimizer, and the output is a set of optimized samples, with the measurements reduced to predefined scalar objectives and the reasons behind success left unarticulated. Here we present SynAgent, a framework in which large language model agents operate an automated experimental system and maintain an explicit, revisable understanding of the synthesis process as the campaign's primary output. Starting with no predefined analysis pipeline, SynAgent adaptively generates analysis skills for newly acquired data and evolves this understanding through multimodal reasoning over experimental data such as X-ray diffraction patterns and electron micrographs. The evolution is guided by a verify-falsify scheme, in which the agent deliberately challenges its own hypotheses by testing conditions predicted to fail as well as those predicted to succeed. In a single campaign of 18 autonomous experiments using LiCoO2 (001) thin-film deposition as a testbed, SynAgent synthesized highly crystalline films and evolved an understanding of how the substrate temperature governs crystallization, discovering an abrupt threshold and a narrow optimal growth window at 650-690 °C. These results extend autonomous experimentation beyond optimized samples to testable, human-readable understanding.
