ARI-MA: Informatics Aided Design of Inorganic Scintillator Materials

该研究奖项的目标是开发一种基于信息学的方法来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形式化方法、第一性原理和中尺度建模、辐射探测特性的测量以及无机闪烁体材料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，用于掺杂物扩散和电荷输运分析，以确定晶体化学和缺陷之间的相互作用，以及它们对信号效率和能量分辨率的影响。实验数据将用于完善我们的统计学习预测，并提供对机制的关键见解，这些机制将指导统计学习分析的解释。预计该项目将导致具有优化性能的新材料，可以显着提高辐射探测器的性能，而不是目前可用的。这项研究也将有助于更好地理解材料化学、晶体学、缺陷和扩散以及辐射探测特性之间的关系。爱荷华州立大学（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将促进这项跨学科的协作工作。这一合作努力将用于开发一个？材料设计培训计划？与LANL合作，培养具有材料信息学和特定设施专业知识的下一代国家设施科学家的研究生和博士后。教育活动包括为高中和本科课程编写新的教材，展示统计学习与材料科学之间的联系。作为该计划的一部分，还将开展教学活动，吸引和促进来自代表性不足群体的学生和教师。

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.