Machine Learning-Based Prediction And Optimization Of Diels-Alder Reaction And Adsorbent

With the development of science and technology,artificial intelligence has gradually been applied to the field of chemistry,enabling more efficient exploration of chemical reaction processes and the design of novel chemical materials.On the one hand,environmental protection and"carbon neutrality"have become highly concerned topics in human society,and the design and preparation of efficient,stable,and low-cost carbon dioxide（CO₂）adsorbents have become key to achieving"carbon neutrality."On the other hand,the Diels-Alder reaction can produce aromatic organic acids and their derivatives from biomass platform compounds,and if these reaction products can be produced on a large scale,they have the potential to replace fossil energy sources and significantly reduce environmental pollution.However,to efficiently obtain products,researchers often need to conduct a large number of repetitive experiments to explore reaction pathways and conditions.Machine learning（ML）can explore specific relationships hidden among factors such as temperature,time,and performance,thereby predicting and analyzing data.Applying machine learning technology in the field of chemistry can help improve research efficiency and reduce experimental consumption.The research content of this paper in this direction is as follows:1.Since there is currently no high-quality qualified dataset to support the research work in this paper,two reliable datasets were established through experimental and literature data collection.By deeply understanding the principles of the Diels-Alder reaction,the properties of reactants and catalysts,reaction temperature,reaction time,and yield were included as part of the Diels-Alder chemical reaction dataset.In addition,related data were collected according to the physicochemical properties of CO₂ adsorbents,including five influencing factors and five evaluation indicators of the adsorption performance of solid amine CO₂ adsorbents,and a dataset of solid amine CO₂ adsorbents was constructed.Molecular descriptors of the corresponding macromolecular compounds in the two datasets were calculated using related tools,making them digital datasets available for machine learning use.2.The optimization and design process of amine CO₂ adsorbents was explored through trained extreme gradient boosting（XGBoost）models and data analysis and prediction were conducted.The effects of six different commercial supports and two common amines（TEPA and PEI）on CO₂ adsorption performance were experimentally studied,and the importance of various factors affecting CO₂ adsorbent performance was ranked,and the interaction between factors was discussed.By predicting the CO₂ adsorption performance of amine-functionalized adsorbents prepared from new supports,important insights and methods were provided for the development and optimization of solid amine CO₂ adsorbents using commercial supports.3.A method was proposed to combine machine learning models with chemical reactions,and achievements were made in yield prediction and experiment optimization by establishing random forest（RF）and deep neural network（DNN）machine learning models.Molecular descriptors with a significant impact on yield prediction were selected through feature importance calculation methods.The random forest model was used for yield prediction and experiment optimization,reducing the number of experiments required to explore suitable catalysts and solvents and improving experimental efficiency.This paper attempts to establish corresponding models for the Diels-Alder chemical reaction and the structure-performance relationship of CO₂ adsorbents through machine learning.An XGBoost model for exploring CO₂ adsorbent design and DNN and RF models for predicting Diels-Alder reactions were built using self-constructed datasets for training.The optimal CO₂ adsorbent design approach was determined,and new methods were provided for Diels-Alder reaction exploration research to improve research efficiency.