发表机构
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出以现象为先的框架，利用LLM在MOF中生成定性行为提案，拓宽机制探索并发现可计算验证的候选现象。
AI 中文摘要
本文介绍了一种以现象为先的材料发现框架，其中大语言模型（LLM）接收关于客体物理、宿主能力和操作环境的独立描述，并提出一种不寻常的定性行为及其机制、区分特征和可证伪条件。在金属有机框架中的80种客体-宿主组合中，模型生成了57个提案，这些提案在评估或材料选择之前被冻结；评估确定了9个计算上可处理的候选方案。在重复运行和仅客体基线的对比中，结构化构建可重复地拓宽了机制探索范围：原始运行和重复运行各覆盖了21个宽泛的机制家族，而仅客体构建仅覆盖8个，尽管提案数量更少。从原始运行中，MC-010在实验报道的MOF中产生了可重复的水合驱动的滞留乙醇重定向，而MC-015产生了相对于匹配的经典描述，H$_2$吸附区域偏好发生量子校正反转的结果。这些结果表明，结构化的大语言模型推理可以将工作流从执行既定目标上移到前瞻性地构建定性的材料现象。
英文摘要
Here we introduce a phenomenon-first framework for materials discovery in which an LLM receives separate descriptions of guest physics, host capabilities and operating context and proposes an unusual qualitative behavior with its mechanism, distinguishing signature and falsifier. Across 80 guest-host combinations in metal-organic frameworks, the model generated 57 proposals that were frozen before evaluation or materials selection; assessment identified nine computationally tractable candidates. In a repeat and guest-only baseline, structured formulation reproducibly broadened mechanistic exploration: the original and repeat runs each covered 21 broad mechanism families versus eight for guest-only formulation despite fewer proposals. From the original run, MC-010 yielded reproducible hydration-driven reorientation of retained ethanol in experimentally reported MOFs, while MC-015 yielded a quantum-corrected reversal of H$_2$ adsorption-region preference relative to the matched classical description. These results show how structured LLM reasoning can move upstream from executing supplied objectives toward prospectively formulating qualitative materials phenomena.
