Informatics-Based Uncertainty Quantification in the Design of Inorganic Scintillators

A soft computing platform, integrating rough sets, fuzzy inferences, and genetic algorithms, is used to develop a series of design rules as a guideline for optimizing inorganic scintillator materials in terms of light yield. The range of values for electrochemical factor, density, Stoke's shift, valence electron factor, and size factor which lead to the highest light yield values are identified, with the range corresponding to the uncertainty in the data. The results presented in this article demonstrate how our approach can address the issues of approximation, vagueness, and uncertainty inherent in a relatively small database. We discuss how the results from this work can be used to enhance previously reported models for predicting light yield.