Data Mining-aided Synthetic Approach

It is well known that the crystallization of microporous crystalline compounds is manipulated by various factors gel composition, reactant sources, solvent, cation, type of template, pH value, crystallization temperature, and crystallization time, etc. Systematically summarizing the experimental data and extracting the essential mles and theories 

Through data-mining techniques, the synthetic factors in determining the synthesis of microporous aluminophosphates, an important family of microporous crystalline materials, have been investigated. Several suitable templates for the formation of aluminophosphates with 12-ring channel structures have been theoretically predicted. 

Data mining is a new methodology for improving the quality and effectiveness of business and scientific decision-making processes.[76] There are currently several data mining techniques available. The decision-tree method is one of the most important techniques.[77] The attribute-splitting criteria of C5.0 decision tree is entropy, which is used to describe uncertainty of a stochastic experiment. X = pi(l), P2(2). pn n) is used to describe a stochastic experiment, in which p, denotes the probability of one experiment whose result is i. So the entropy function can be expressed as Equation (7.5)  

Previously used template Number of templates/unit Energy/(kJ/mol) 

The rules to predict AlP04-5 (AFI) synthesis are preliminarily built by data mining. The results associate with six attributes, involving the longest atomic distance 0.496 nm, the secondary distance 0.765 nm, the ratio of the number of protons acceptable by the template to the number of N atoms 8, and the formation enthalpy 421.41 kJ/mol. The reliability of the constraint is 178/190 = 93.7%, and the support-ability of that is 190/549 = 34.6%. 

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