用大模型生成材料设计假说，加速新材料发现。
Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents
- 基于真实科研目标与约束，让大模型生成可验证的设计假说。
- 构建新数据集并提出可扩展的评估方法，提升假说质量判断效率。
- 适合材料科学与AI交叉研究者，推动智能材料研发进程。
材料发现与设计对多个产业的技术进步至关重要，可实现特定应用材料的开发。近期研究利用大语言模型（LLMs）加速该过程。我们探索了LLM生成可行假说的潜力，这些假说经验证后可加快材料发现。通过与材料科学家合作，我们从近期期刊论文中构建了一个新数据集，包含真实世界的目标、约束及设计方法。基于此数据集，测试了能够生成满足特定目标和约束条件下假设的LLM代理。为评估假设的相关性与质量，我们提出一种新颖的可扩展评估指标，模拟材料科学家批判性评价假说的过程。所提出的数据集、方法与评估框架旨在推动未来利用LLMs加速材料发现与设计的研究。
原文摘要 · Abstract (English)
Materials discovery and design are essential for advancing technology across various industries by enabling the development of application-specific materials. Recent research has leveraged Large Language Models (LLMs) to accelerate this process. We explore the potential of LLMs to generate viable hypotheses that, once validated, can expedite materials discovery. Collaborating with materials science experts, we curated a novel dataset from recent journal publications, featuring real-world goals, constraints, and methods for designing real-world applications. Using this dataset, we test LLM-based agents that generate hypotheses for achieving given goals under specific constraints. To assess the relevance and quality of these hypotheses, we propose a novel scalable evaluation metric that emulates the process a materials scientist would use to evaluate a hypothesis critically. Our curated dataset, proposed method, and evaluation framework aim to advance future research in accelerating materials discovery and design with LLMs.
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