用大模型引导符号回归，高效发现材料物理规律
Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression
- 利用大语言模型的科学知识指导符号回归搜索
- 搜索空间缩小约10万倍，成功发现新材料性质公式
- 结果兼具物理意义、精度高，适合材料与算法交叉研究者
从高维数据中发现可解释的物理规律是科学研究的核心挑战。传统符号回归在庞大可能形式空间中常产生复杂且不合理的公式。本文提出一种新框架，借助大语言模型嵌入的科学知识引导搜索过程，实现对钙钛矿材料关键性质的高效建模。该方法有效缓解了传统符号回归常见的组合爆炸问题，使有效搜索空间缩小约10^5倍。我们识别出一组关于体模量、带隙和氧析出反应活性的新公式，不仅具有明确的物理解释，且在准确性和简洁性上均优于已有公式。
原文摘要 · Abstract (English)
Discovering interpretable physical laws from high-dimensional data is a fundamental challenge in scientific research. Traditional methods, such as symbolic regression, often produce complex, unphysical formulas when searching a vast space of possible forms. We introduce a framework that guides the search process by leveraging the embedded scientific knowledge of large language models, enabling efficient identification of physical laws in the data. We validate our approach by modeling key properties of perovskite materials. Our method mitigates the combinatorial explosion commonly encountered in traditional symbolic regression, reducing the effective search space by a factor of approximately $10^5$. A set of novel formulas for bulk modulus, band gap, and oxygen evolution reaction activity are identified, which not only provide meaningful physical insights but also outperform previous formulas in accuracy and simplicity.
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