用大模型找新材料，靠类比推理突破传统思路
LacMaterial: Large Language Models as Analogical Chemists for Materials Discovery
- 用跨领域类比启发新材料设计，避开常规掺杂方式
- 仅用少量标注样例构建类比模板，生成超出传统组合空间的候选材料
- 适合材料科学与人工智能交叉研究者参考
类比推理（如行星绕太阳类比电子绕原子核）是科学发现的核心。然而人类认知常受限于领域专长和表层偏见，难以挖掘深层结构类比。大语言模型（LLMs）在海量跨领域数据上训练，具备潜在的类比推理能力。本文证明，LLMs可通过（1）检索跨领域类比和类比引导的范例，引导探索超越传统掺杂替换的新路径；（2）从少量标注样本构建领域内类比模板，实现定向挖掘。这些显式类比策略生成的材料候选超出既有化学组成空间，性能优于标准提示基线。结果表明，LLMs可作为可解释、类专家的假说生成器，通过类比泛化推动科学创新。
原文摘要 · Abstract (English)
Analogical reasoning, the transfer of relational structures across contexts (e.g., planet is to sun as electron is to nucleus), is fundamental to scientific discovery. Yet human insight is often constrained by domain expertise and surface-level biases, limiting access to deeper, structure-driven analogies both within and across disciplines. Large language models (LLMs), trained on vast cross-domain data, present a promising yet underexplored tool for analogical reasoning in science. Here, we demonstrate that LLMs can generate novel battery materials by (1) retrieving cross-domain analogs and analogy-guided exemplars to steer exploration beyond conventional dopant substitutions, and (2) constructing in-domain analogical templates from few labeled examples to guide targeted exploitation. These explicit analogical reasoning strategies yield candidates outside established compositional spaces and outperform standard prompting baselines. Our findings position LLMs as interpretable, expert-like hypothesis generators that leverage analogy-driven generalization for scientific innovation.
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