Abstract
The relationship between synthetic factors and the resulting structures is critical for rational synthesis of zeolites and related microporous materials. In this paper, we develop a new feature selection method for synthetic factor analysis of (6,12)-ring-containing microporous aluminophosphates (AlPOs). The proposed method is based on a maximum weight and minimum redundancy criterion. With the proposed method, we can select the feature subset in which the features are most relevant to the synthetic structure while the redundancy among these selected features is minimal. Based on the database of AlPO synthesis, we use (6,12)-ring-containing AlPOs as the target class and incorporate 21 synthetic factors including gel composition, solvent and organic template to predict the formation of (6,12)-ring-containing microporous aluminophosphates (AlPOs). From these 21 features, 12 selected features are deemed as the optimized features to distinguish (6,12)-ring-containing AlPOs from other AlPOs without such rings. The prediction model achieves a classification accuracy rate of 91.12% using the optimal feature subset. Comprehensive experiments demonstrate the effectiveness of the proposed algorithm, and deep analysis is given for the synthetic factors selected by the proposed method.
Highlights
As an important class of crystalline materials, zeolites and related microporous materials have been widely used in the petroleum industry for catalysis, separation and ion-exchange [1,2]
A novel feature selection method based on maximum weight and minimum redundancy criterion is proposed
Comprehensive experiments and deep analysis based on the microporous aluminophosphates (AlPOs) database demonstrate the effectiveness of the proposed algorithm
Summary
As an important class of crystalline materials, zeolites and related microporous materials have been widely used in the petroleum industry for catalysis, separation and ion-exchange [1,2]. The rational synthesis of microporous inorganic materials has attracted extensive attention [3,4,5,6,7,8,9,10]. With the rapid development of computer technology and artificial intelligence, data mining plays an increasingly important role in more and more research areas. The applications of data mining techniques in chemical science have shown their feasibility for numeric calculation, simulation and data analysis. One of the most widely used data mining techniques in chemical science is feature selection. Several feature selection methods were successfully applied in chemical data analysis. Pichler [13] developed an interactive feature selection method based on KNN (K Nearest Neighbor) to classify doublet/singlet patterns from the same Stationary Electrode Polarography (SEP) data. Liu evaluated the performance of the methods as Information Gain, Mutual Information, χ2-Test (CHI), Odds Ratio (OR) and GSS
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
