---
title: "DASyR-LLM：用大语言模型注入领域知识的化学动力学模型发现框架"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.05120v1"
permalink: "https://feed.astralor.com/items/itm_61e9ec2c35bb7e8e47211f86"
published: "2026-08-05T17:48:45.000Z"
score: 72
channel: "tech"
tags: ["DASyR-LLM","动力学模型","化学工程","大语言模型","符号回归"]
---

# DASyR-LLM：用大语言模型注入领域知识的化学动力学模型发现框架

## AI 点评

> 这篇论文的价值在于把LLM从'写代码'延伸到'做科学假设'，对实验成本高昂的化工领域尤其重要。如果你从事动力学建模或AI4Science，值得拿来对比自己的搜索策略；即便不是本行，也能看到领域知识注入AI的一种高效范式。

## 速览

化学工程中动力学模型发现依赖准确的速率表达式，但传统符号回归常缺乏领域知识，易探索出物化上不合理的模型。该研究提出DASyR-LLM，将大语言模型嵌入迭代式符号回归算法，由LLM承担候选模型定性物理化学批判与基于化学知识提出新表达式的双重角色。在四个体外案例中将找真模型的迭代数减少41.7%-79.3%，超半数运行中LLM直接提出正确结构，同时独立验证集预测性能保持R²>0.98。这显著降低湿实验成本，为自动化领域感知建模铺路。

## 关键点

- 提出DASyR-LLM框架，将LLM嵌入迭代符号回归，兼具批判与生成双重角色。
- 在异相催化与生物过程四个案例中，找真模型迭代数减少41.7%-79.3%。
- 超过一半运行中LLM直接提出正确模型结构，且预测精度保持R²>0.98。
- 消融实验表明SR组件与LLM规模均有贡献，小型LLM仍能保持较高发现效率。

## 全文

Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological processes. Symbolic regression (SR) has emerged as a powerful data-driven approach for identifying interpretable kinetic models, but usually operates without domain knowledge, often exploring physicochemically implausible models. Large language models (LLMs) offer a promising avenue for injecting domain expertise into this search. Here, we introduce an LLM-guided SR framework, embedding an LLM module within an iterative SR algorithm for automated kinetic model discovery. The LLM performs two roles at each iteration: (1) a qualitative physicochemical critique of the best SR candidates, and (2) the proposal of new candidate rate expressions guided by the SR-generated models and embedded chemical knowledge. Our framework is evaluated on four in silico case studies of increasing complexity, spanning heterogeneous catalysis and bioprocess systems. Results show the LLM-guided framework reduces iterations to identify the ground-truth model by $41.7-79.3\%$ versus a state-of-the-art SR framework, with the LLM directly proposing the correct model structure in over half of the guided runs. In practical settings, where each iteration typically requires a new wet-lab experiment, this translates into a substantial reduction in experimental effort. Predictive performance on an independent validation set is equivalent between both approaches, with $R^2>0.98$ in all case studies. Ablation studies indicate that both the SR component and the LLM scale contribute to this performance, with a reduced-size LLM largely retaining discovery efficiency. These findings demonstrate that LLMs can effectively inject domain knowledge into scientific model discovery, paving the way toward fully automated, domain-aware kinetic modelling pipelines.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_61e9ec2c35bb7e8e47211f86