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[2602.22967] Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression
Summary
This paper presents a novel framework that utilizes language models to guide symbolic regression in discovering interpretable physical laws from high-dimensional data, specifically applied to perovskite materials.
Why It Matters
The ability to derive interpretable physical laws from complex data is crucial for advancing scientific research. This study addresses the limitations of traditional symbolic regression by leveraging language models, potentially transforming how researchers analyze material properties and enhancing the accuracy and simplicity of derived formulas.
Key Takeaways
- Introduces a framework that combines language models with symbolic regression.
- Reduces the search space for physical laws by approximately 100,000 times.
- Identifies new formulas for key properties of perovskite materials.
- Enhances accuracy and interpretability of physical laws derived from data.
- Addresses challenges in traditional symbolic regression methods.
Physics > Computational Physics arXiv:2602.22967 (physics) [Submitted on 26 Feb 2026] Title:Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression Authors:Yifeng Guan, Chuyi Liu, Dongzhan Zhou, Lei Bai, Wan-jian Yin, Jingyuan Li, Mao Su View a PDF of the paper titled Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression, by Yifeng Guan and 6 other authors View PDF HTML (experimental) Abstract: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. Subjects: Computational Physics (physics.comp-ph); Artificial Intelligence (c...
