EAGER: Exploratory Research in Automated Computational Analysis of Inorganic Materials Libraries

EAGER：无机材料库自动计算分析的探索性研究

• 批准号: 1258330
• 负责人: Carla Gomes
• 金额: $ 13.34万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1258330&HistoricalAwards=false
• 关键词: EAGER; Exploratory; Research; Automated; Computational

Combinatorial Materials Science represents a potentially powerful approach to identifying new and unexpected materials. This involves the rapid, high-throughput synthesis, measurement, and analysis of a large number of different materials. Understanding the functional behavior of the materials requires a characterization of the structure-property relations. Crystalline structure information can be obtained through X-ray diffraction studies. An unsolved challenge is to develop automated techniques for identification of unique diffraction patterns and to cluster the resulting patterns into contiguous phase fields corresponding to regions with different material composite structures. Intellectual Merit: This exploratory project is aimed at establishing the feasibility of a unique interdisciplinary approach, involving a team of materials scientists and computer scientists, to address the challenge of structure (crystalline phase) identification of the composite materials. Specifically, the PIs propose to extract the key diffraction pattern features from the raw experimental data as a first step towards the development of computational methods for the identification of crystalline phases. Broader Impacts: The project, if successful, will establish the feasibility of a key first step in an overall methodology to significantly speed the materials scientific discovery process in general, and in the search for new materials for the next generation fuel-cell technology in particular. The project brings together faculty and students, providing training in materials science, engineering, and computer science.

组合材料科学代表了一种潜在的强有力的方法来识别新的和意想不到的材料。这涉及到大量不同材料的快速、高通量合成、测量和分析。理解材料的功能行为需要表征结构-性能关系。晶体结构信息可以通过X射线衍射研究获得。未解决的挑战是开发用于识别独特衍射图案的自动化技术，并将所得图案聚类成对应于具有不同材料复合结构的区域的连续相场。智力优势：该探索性项目旨在建立一种独特的跨学科方法的可行性，涉及材料科学家和计算机科学家团队，以解决复合材料结构（晶相）识别的挑战。具体而言，PI建议从原始实验数据中提取关键的衍射图案特征，作为开发用于鉴定晶相的计算方法的第一步。更广泛的影响：该项目如果成功，将确定整体方法的关键第一步的可行性，以显着加快材料科学发现过程，特别是为下一代燃料电池技术寻找新材料。该项目汇集了教师和学生，提供材料科学，工程和计算机科学方面的培训。