辽宁石油化工大学学报2026，Vol.46Issue(2)：78-87,10.DOI:10.12422/j.issn.1672-6952.2026.02.009
基于大语言模型与深度学习的CO2加氢制甲醇催化剂性能筛选与预测
Performance Prediction of Catalysts for CO2 Hydrogenation to Methanol Based on Large Language Model and Deep Learning
摘要
Abstract
To address the low efficiency in developing catalysts for CO2 hydrogenation to methanol,this study constructs and validates an intelligent performance prediction model based on large language model(LLM)and deep learning.First,a Large Language Model(LLM)to design structured prompts,achieving semi-automated and high-efficiency extraction of multi-dimensional catalyst data from literature.Subsequently,a Wasserstein Generative Adversarial Network with Gradient Penalty(WGAN-GP)is employed to augment the sparse original dataset,effectively overcoming the bottleneck of data scarcity.Following data cleaning,feature engineering,and dimensionality reduction,a hyperparameter-optimized Multi-Layer Perceptron(MLP)is constructed as the prediction model.The results show that the optimized MLP model achieves high prediction accuracy on an independent test set,with R² values for CO2 conversion and methanol selectivity reaching as high as 0.972 3 and 0.969 3,respectively.SHAP-based feature analysis reveals that BET surface area and Cu-based catalysts are the dominant factors affecting catalytic performance,and also uncovered the unique dependency of In-based catalysts on metal content.This data-driven model,integrating LLM and WGAN-GP,provides a powerful tool for the rapid screening and rational design of novel catalysts,demonstrating the great potential of AI in catalysis research.关键词
CO2加氢/甲醇合成/催化剂性能预测/大语言模型/机器学习Key words
CO2 hydrogenation/Methanol synthesis/Catalyst performance prediction/Large language model/Machine learning分类
化学化工引用本文复制引用
刘庆辉,李子怡,余皓,杨思宇..基于大语言模型与深度学习的CO2加氢制甲醇催化剂性能筛选与预测[J].辽宁石油化工大学学报,2026,46(2):78-87,10.基金项目
国家自然科学基金重点项目(U22A20415,22278151) （U22A20415,22278151）
广东省基础与应用基础研究基金项目(2023A1515012071). （2023A1515012071）
