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Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression
arXiv – CS AI|Yifeng Guan, Chuyi Liu, Dongzhan Zhou, Lei Bai, Wan-jian Yin, Jingyuan Li, Mao Su||6 views
🤖AI Summary
Researchers have developed a new framework that uses large language models to guide symbolic regression in discovering interpretable physical laws from high-dimensional materials data. The method reduces the search space by approximately 10^5 times compared to traditional approaches and successfully identified novel formulas for key properties of perovskite materials.
Key Takeaways
- →Large language models can effectively guide symbolic regression to discover physical laws in materials science data.
- →The new framework reduces the combinatorial explosion problem by a factor of approximately 10^5 compared to traditional methods.
- →Novel formulas were identified for bulk modulus, band gap, and oxygen evolution reaction activity in perovskite materials.
- →The discovered formulas provide both meaningful physical insights and superior accuracy compared to previous approaches.
- →This represents a significant advancement in automated scientific discovery using AI-guided search methods.
#artificial-intelligence#machine-learning#symbolic-regression#materials-science#scientific-discovery#language-models#perovskite#physical-laws#research#automation
Read Original →via arXiv – CS AI
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