ARI-MA: Informatics Aided Design of Inorganic Scintillator Materials

该研究奖的目的是开发一种基于信息学的方法，以加速设计和发现新的辐射探测器材料。该研究将整合信息论中统计学习的正式方法，以第一原理和介观建模，辐射探测特性的测量，以及无机闪烁体材料中缺陷的新型高通量筛选和建模研究。 该计划的实验部分将引入一种新的原子尺度组合筛选技术，用于掺杂剂扩散和电荷传输分析，以确定晶体化学和缺陷之间的相互作用，以及它们在影响信号效率和能量分辨率方面的作用。实验数据将用于完善我们的统计学习预测，以及提供关键的洞察机制，将指导统计学习分析的解释。预计该项目将导致新材料的优化性能，可以显着提高辐射探测器的性能超越目前可用的。 这项研究也将有助于更好地了解材料化学，晶体学，缺陷和扩散，辐射探测特性之间的关系。这种跨学科的合作工作将由爱荷华州州立大学（ISU）、凯斯西储大学（CWRU）和洛斯阿拉莫斯国家实验室（LANL）之间的数据共享网络基础设施来促进。这一合作努力将用于开发一个？材料设计培训计划？为研究生和博士后研究员与LANL合作，培养下一代的国家设施科学家与材料信息学和设施特定的专业知识。教育活动包括为高中和本科课程开发新的教材，展示统计学习和材料科学之间的联系。教学活动，以吸引和促进学生和教师从代表性不足的群体也将开发作为该计划的一部分。

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.