Large Language Models have recently gained significant attention in scientific discovery for their extensive knowledge and advanced reasoning capabilities. However, they encounter challenges in effectively simulating observational feedback and grounding it with language to propel advancements in physical scientific discovery. Conversely, human scientists undertake scientific discovery by formulating hypotheses, conducting experiments, and revising theories through observational analysis. Inspired by this, we propose to enhance the knowledge-driven, abstract reasoning abilities of LLMs with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework: LLMs act as knowledgeable and versatile thinkers, proposing scientific hypotheses and reason about discrete components, such as physics equations or molecule structures; meanwhile, simulations function as experimental platforms, providing observational feedback and optimizing via differentiability for continuous parts, such as physical parameters. We conduct extensive experiments to demonstrate our framework's efficacy in constitutive law discovery and molecular design, unveiling novel solutions that differ from conventional human expectations yet remain coherent upon analysis.

翻译：大型语言模型凭借其广泛的知识和先进的推理能力，近期在科学发现领域获得了显著关注。然而，它们在有效模拟观测反馈并将其与语言相结合以推动物理科学发现方面仍面临挑战。相比之下，人类科学家通过提出假设、开展实验并基于观测分析修正理论来完成科学发现。受此启发，我们提出利用模拟的计算能力增强LLM的知识驱动型抽象推理能力。我们引入了科学生成智能体（SGA）这一双层优化框架：LLM充当博学多能的思考者，负责提出科学假设并推理离散组件（如物理方程或分子结构）；而模拟则作为实验平台，提供观测反馈并通过可微性优化连续部分（如物理参数）。我们通过大量实验证明该框架在构建本构定律发现与分子设计中的有效性，揭示出不同于传统人类预期但经分析仍具自洽性的新颖解决方案。