DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery
Score
72提出LLM与SR结合的新框架，并给出明确的迭代效率量化收益，信息增量显著。
实验设计完整，包含多案例验证、消融实验与独立验证集，证据链条扎实。
减少湿实验迭代次数的特性对化工与生物过程建模人员具有直接实用价值。
契合LLM驱动科学发现的热点方向，但作为预印本尚未经过完整同行评审。
来源为arXiv预印本，方法描述清晰且结果可验证，可信度较高。
Base 80.5 × T2 Source weight ×0.9 = 72
Curation line 70Selected
Why selected
The work is novel and experimentally rigorous, offering high reference value for AI-assisted scientific discovery and chemical engineering modeling.
AI commentary
「This paper is valuable because it extends LLMs from writing code to making scientific hypotheses, especially for chemical fields where wet-lab experiments are costly. If you work on kinetic modeling or AI4Science, it is worth comparing with your own search strategy; even outside the field, it showcases an efficient paradigm for injecting domain knowledge into AI.」
Quick overview
Kinetic model discovery in chemical engineering depends on accurate rate expressions, yet traditional symbolic regression often lacks domain knowledge and tends to explore physicochemically implausible models. This study introduces DASyR-LLM, which embeds a large language model into an iterative symbolic regression algorithm. The LLM plays dual roles: providing qualitative physicochemical critiques of candidate models and proposing new rate expressions guided by chemical knowledge. Across four in silico case studies, the framework reduces iterations needed to identify the ground-truth model by 41.7%-79.3%, with the LLM directly proposing correct model structures in over half of guided runs, while maintaining equivalent predictive performance (R²>0.98) on independent validation sets. This substantially reduces wet-lab experimental effort and paves the way for fully automated, domain-aware kinetic modeling pipelines.
- Introduces DASyR-LLM, an LLM-guided symbolic regression framework with dual roles of critique and generation.
- Reduces iterations to identify ground-truth models by 41.7%-79.3% across four catalysis and bioprocess case studies.
- LLM directly proposes the correct model structure in over half of guided runs while maintaining R²>0.98.
- Ablation studies show both the SR component and LLM scale matter, with a smaller LLM retaining discovery efficiency.
Full text
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