ARI-MA: Informatics Aided Design of Inorganic Scintillator Materials

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The objective of this research award is to develop an informatics based approach to the accelerated design and discovery of new radiation detector materials. The research will integrate the formal methods of statistical learning in information theory to first-principles and mesoscale modeling, measurements of radiation detection characteristics, and novel high throughput screening and modeling studies of defects in inorganic scintillator materials. The experimental component of the program will introduce a novel atomic scale combinatorial screening technique for dopant diffusion and charge transport analysis to identify the interaction between crystal chemistry and defects, and their role in affecting signal efficiency and energy resolution. The experimental data will be used to refine our statistical learning predictions, as well as provide critical insight into mechanisms that will guide the interpretation of the statistical learning analysis.It is expected that this project will lead to new materials with optimized properties that can significantly improve radiation detector performance beyond those presently available. This research will also contribute to a better understanding of the relationship between materials chemistry, crystallography, defects and diffusion, and radiation detection characteristics. This interdisciplinary, collaborative work will be facilitated by a cyberinfrastructure for data sharing between Iowa State University (ISU), Case Western Reserve University (CWRU) and Los Alamos National Laboratory (LANL. This cooperative effort will be used to develop a ?Materials Design Traineeship Program? for graduate students and post doctoral fellows in cooperation with LANL to train the next generation of national facilities scientists with both materials informatics and facility-specific expertise. Educational activities include the development of novel teaching materials for high schools and undergraduate courses that demonstrate the connections between statistical learning and materials science. Teaching activities to engage and promote students and faculty from underrepresented groups will also be developed as part of this program.