Prediction of ITQ-21 zeolite phase crystallinity: Parametric versus non-parametric strategies

This work deals with data analysis techniques and high-throughput tools for synthesis and characterization of solid materials. In previous studies, it was found that the final properties of materials could be successfully modeled using learning systems. Machine learning algorithms such as neural networks, support vector machines, and regression trees are non-parametric strategies. They are compared to traditional parametric statistical approaches. We review a wide range of statistical methodologies, and all the methods are evaluated using experimental data derived from an exploration-optimization of the material ITQ-21. The results are judged on the numerical prediction of phase's crystallinity. We discuss the theoretical aspects of such statistical techniques, which make them an attractive method when compared to other learning strategies for modeling the properties of the solids. Advantages and drawbacks are highlighted. We show that such approaches, by offering broad solutions, can reach high-level performances while offering ease of use, comprehensibility, and control. Finally, we shed light on both the interpretation and stability of results, which remain the main drawbacks of the majority of machine learning methodologies when trying to retrieve knowledge from the data treatment.