CDI Type I: Collaborative research: Materials Informatics: Computational tools for discovery and design

CDI I 型：协作研究：材料信息学：用于发现和设计的计算工具

• 批准号: 0940218
• 负责人: Yousef Saad
• 金额: $ 34.61万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0940218&HistoricalAwards=false
• 关键词: CDI; Type; Collaborative; research; Materials

This award is made on a proposal submitted to the Cyberenabled Discovery and Innovation initiative. The goal of this CDI project is to explore new, transformative strategies that will exploit recent advances in quantum modeling algorithms and software, data mining techniques, and high-performance hardware, for the discovery and design of materials for a wide variety of applications. While the number of experimentally known binary materials is fairly complete, of the roughly 160,000 possible ternary materials only about 5% are known and of the possible 4 million quaternary materials less than 1% are known. The potential for finding a new material in this realm is great, e.g., a superhard material, a new catalytic material, or an efficient photovoltaic material could be residing in this set of unexplored materials. An efficient search for special materials in this myriad of possible combinations is a daunting task. Successful searching procedures combined with effective computational methods to evaluate the properties of a candidate material could have a tremendous impact as theoretical methods for materials discovery challenge experimental ones. Materials scientists would be able to examine postulated materials on a routine basis and predict their properties without resort to experiment. Synthesis of novel materials would be greatly enhanced. The research will implement methods in the category of materials informatics. The focus will be on combining data mining with quantum mechanical methods for computing properties of materials to address "grand challenge" problems such as how to engineer semiconductors with desired electronic properties, thermoelectricity, and catalysis. The cross-disciplinary theme of the proposed work will rely on the expertise of the PIs in three main areas that are vital to its success: materials science, data mining, and high-performance computing. This award supports the PIs' commitment to a vigorous program of attracting underrepresented groups. Owing to the relatively new approach of informatics applied to materials, it is imperative to train students and researchers in both data mining and quantum modeling. The PIs' activities will center on the following efforts:Alice in Wonderland Program. This teaching activity will involve an active effort to recruit members of underrepresented groups at the high school level to participate in materials research activities. The Alice in Wonderland program is coordinated at the University of Texas. The goal of this program is to attract female high school students to physics, materials science or chemical engineering by involving them in research over the summer before they make decisions about colleges. The high school students attend a short course given at the start of the program by graduate students and work in research labs under the mentorship of the PIs. Summer Undergraduate Interns. Under prior NSF support, the PIs initiated a program with the Minnesota Supercomputing Institute to recruit undergraduate interns interested in high performance computing for summer internships. The interns will be located at the University of Minnesota and will participate in this project?s research activities. Graduate Education and Training. The number of multidisciplinary efforts which focus on problems in the materials has increased at a rapid pace in recent years. At the same time, the number of students who receive training on the effective use of informatics software for materials is small. To address this issue, the PIs will design new courses, outside of the current curriculum, to meet this need and train graduate students in this new field. Students will exchange mentors between the PIs to ensure that each will have a knowledge of informatics and materials modeling.

该奖项是根据提交网络培养发现和创新计划提交的提案颁发的。该CDI项目的目的是探索新的变革性策略，这些策略将利用量子建模算法和软件，数据挖掘技术和高性能硬件的最新进展，以发现和设计各种应用程序的材料。尽管实验已知的二进制材料的数量相当完整，但在大约160,000个可能的三元材料中，只有约5％的材料已知，而在可能的400万季四元材料中，较少1％的四元材料已知。在该领域中找到新材料的潜力非常好，例如，可以驻留在这套未探索的材料中，例如，超级材料，新的催化材料或有效的光伏材料。在这种无数可能的组合中，有效地搜索特殊材料是一项艰巨的任务。成功的搜索程序与有效的计算方法相结合，以评估候选材料的性质可能会产生巨大的影响，作为材料发现挑战实验的理论方法。材料科学家将能够常规检查假定的材料，并在没有实验的情况下预测其性质。新型材料的合成将大大增强。该研究将在材料信息学类别中实施方法。重点将是将数据挖掘与用于计算材料特性的量子机械方法相结合，以解决“大挑战”问题，例如如何使用所需的电子特性，热电和催化来设计具有所需电子性能的半导体。拟议工作的跨学科主题将依赖于三个主要领域的PIS专业知识，这些领域对其成功至关重要：材料科学，数据挖掘和高性能计算。该奖项支持PIS对吸引人数不足的群体的积极计划的承诺。由于适用于材料的信息学方法相对较新的方法，必须培训学生和研究人员进行数据挖掘和量子建模。 PIS的活动将以以下努力为中心：爱丽丝梦游仙境计划。这项教学活动将涉及一项积极的努力，以招募高中一级代表性不足的群体的成员参加材料研究活动。爱丽丝梦游仙境计划在德克萨斯大学协调。该计划的目的是通过在夏季参与研究，然后他们就大学做出决定，以吸引女性高中生进入物理，材料科学或化学工程。高中生参加了研究生在课程开始时的简短课程，并在PIS的指导下在研究实验室工作。夏季本科实习生。在先前的NSF支持下，PIS与明尼苏达州超级计算研究所启动了一项计划，以招募对暑期实习感兴趣的本科实习生。实习生将位于明尼苏达大学，将参加该项目的研究活动。研究生教育和培训。近年来，关注材料问题的多学科工作的数量已迅速增加。同时，接受有关材料有效使用信息软件培训的学生数量很少。为了解决这个问题，PI将在当前课程之外设计新课程，以满足这一需求，并在这个新领域培训研究生。学生将在PI之间交换导师，以确保每个人都了解信息学和材料建模。