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网络支持的发现和创新倡议的一项提案而颁发的。CDI项目的目标是探索新的、变革性的战略，这些战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%。在这一领域找到新材料的潜力很大，例如，一种超硬材料、一种新的催化材料或一种高效的光伏材料可能存在于这一组未探索的材料中。在这无数种可能的组合中高效地寻找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对实验方法提出了挑战。材料科学家将能够在常规的基础上检查假设的材料，并预测它们的性能，而不需要诉诸实验。新材料的合成将得到极大的加强。本研究将实施材料信息学范畴的研究方法。重点将放在将数据挖掘与计算材料性质的量子力学方法相结合，以解决诸如如何设计具有所需电子性能、热电和催化的半导体等“重大挑战”问题。拟议工作的跨学科主题将依赖于对其成功至关重要的三个主要领域的专业知识：材料科学、数据挖掘和高性能计算。这一奖项支持了私人投资机构致力于积极吸引代表性不足群体的计划。由于信息学应用于材料的方法相对较新，因此对学生和研究人员进行数据挖掘和量子建模方面的培训势在必行。PI的活动将集中在以下几个方面：爱丽丝梦游仙境计划。这项教学活动将积极努力招募高中一级代表性不足群体的成员参加材料研究活动。《爱丽丝梦游仙境》项目由德克萨斯大学负责协调。这个项目的目标是吸引女高中生学习物理、材料科学或化学工程，方法是让她们在暑假里参与研究，然后再决定是否上大学。高中生在项目开始时参加由研究生开设的短期课程，并在私人投资机构的指导下在研究实验室工作。暑期本科实习生。在之前NSF的支持下，PI与明尼苏达超级计算研究所共同发起了一项计划，招募对高性能计算感兴趣的本科生实习生进行暑期实习。实习生将在明尼苏达大学实习，并将参加S项目的研究活动。研究生教育与培训。近年来，专注于材料问题的多学科努力的数量迅速增加。同时，接受有效使用信息学软件材料培训的学生人数很少。为了解决这一问题，专业督学将在现有课程之外设计新的课程，以满足这一需求，并培训研究生从事这一新领域的工作。学生将在PI之间交换导师，以确保每个PI都有信息学和材料建模的知识。