CDI-TYPE I-COLLABORATIVE Materials Informatics: Computational Tools for Discovery and Design

CDI-TYPE I-COLLABORATIVE 材料信息学：用于发现和设计的计算工具

• 批准号: 0941645
• 负责人: James Chelikowsky
• 金额: $ 35.76万
• 依托单位: University of Texas at Austin
• 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=0941645&HistoricalAwards=false
• 关键词: CDI; TYPE; COLLABORATIVE; Materials; Informatics

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.

该奖项是根据提交给Cyberable Discovery and Innovation倡议的提案颁发的。该CDI项目的目标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，为各种应用发现和设计材料。虽然实验上已知的二元材料的数量相当完整，但在大约160，000种可能的三元材料中，只有大约5%是已知的，而在可能的400万种四元材料中，已知的不到1%。在这一领域发现新材料的潜力是巨大的，例如，超硬材料、新的催化材料或有效的光电材料可以存在于这组未开发的材料中。在这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。成功的搜索程序结合有效的计算方法来评估候选材料的性能可能会产生巨大的影响，因为材料发现的理论方法挑战了实验方法。材料科学家将能够在常规基础上检查假设的材料，并预测它们的性质，而无需借助实验。新材料的合成将大大提高。该研究将在材料信息学的范畴内实施方法。重点将是将数据挖掘与量子力学方法相结合，用于计算材料的特性，以解决“重大挑战”问题，例如如何设计具有所需电子特性的半导体，热电和催化。拟议工作的跨学科主题将依赖于PI在三个对其成功至关重要的主要领域的专业知识：材料科学，数据挖掘和高性能计算。该奖项支持PI致力于吸引代表性不足的群体的积极计划。由于信息学应用于材料的相对较新的方法，因此必须对学生和研究人员进行数据挖掘和量子建模方面的培训。PI的活动将集中在以下方面：爱丽丝梦游仙境计划。这一教学活动将包括积极努力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是在德克萨斯大学协调。该计划的目标是吸引女高中生学习物理、材料科学或化学工程，让她们在做出大学决定之前在夏天参与研究。高中生在项目开始时参加研究生提供的短期课程，并在PI的指导下在研究实验室工作。暑期本科实习生。在NSF之前的支持下，PI与明尼苏达超级计算研究所发起了一项计划，招募对高性能计算感兴趣的本科实习生进行暑期实习。实习生将在明尼苏达大学，并将参加这个项目？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努力的数量迅速增加。与此同时，接受有效使用材料信息学软件培训的学生人数很少。为了解决这个问题，PI将设计新的课程，目前的课程之外，以满足这一需求，并在这个新的领域培养研究生。学生将在PI之间交换导师，以确保每个人都有信息学和材料建模的知识。