The formation of mesopores in microporous zeolites is generally performed by postsynthesis acid, basic, and steam treatments. The hierarchical pore systems thus formed allow better adsorption, diffusion, and reactivity of these materials. By combining organic and inorganic structure-directing agents and high-throughput methodologies, we were able to synthesize a zeolite with a hierarchical system of micropores and mesopores, with channel openings delimited by 28 tetrahedral atoms. Its complex crystalline structure was solved with the use of automated diffraction tomography.

This works provides an introduction to support vector machines (SVMs) for predictive modeling in heterogeneous catalysis, describing step by step the methodology with a highlighting of the points which make such technique an attractive approach. We first investigate linear SVMs, working in detail through a simple example based on experimental data derived from a study aiming at optimizing olefin epoxidation catalysts applying high-throughput experimentation. This case study has been chosen to underline SVM features in a visual manner because of the few catalytic variables investigated. It is shown how SVMs transform original data into another representation space of higher dimensionality. The concepts of Vapnik-Chervonenkis dimension and structural risk minimization are introduced. The SVM methodology is evaluated with a second catalytic application, that is, light paraffin isomerization. Finally, we discuss why SVMs is a strategic method, as compared to other machine learning techniques, such as neural networks or induction trees, and why emphasis is put on the problem of overfitting.

Full PaperThe work presents for the first time a detailed methodology which enable to scrutinize and identify solids that are relevant to be tested in a high throughput program. In the present case study, Artificial Neural Networks (ANN) are used to predict performances of catalysts for the Water Gas Shift reaction. In contrast to previous studies, it is shown that the quantitative prediction by ANN of performances is not adapted to a primary screening stage.On the contrary, ANN used as classifier tool within the course of an Evolutionary Strategy are well performing and well suited for high throughput heterogeneous catalysis. The virtual screening enables to pre-select candidates to be screened experimentally with a very high rate of relevance at an early stage of a high throughput experimentation program, thus reducing significantly the number of trials.