CAREER: Understanding Strong Correlations in Real Materials with Scalable High Performance Computing

职业：通过可扩展的高性能计算了解真实材料中的强相关性

• 批准号: 9734041
• 负责人: Shiwei Zhang
• 金额: $ 20万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-06-01 至 2003-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9734041&HistoricalAwards=false
• 关键词: CAREER; Understanding; Strong; Correlations; Real

9734041 Zhang This is a CAREER award which integrates research and education in computational materials physics. The research will address long-standing questions on electron correlations in high temperature superconductors. Intense theoretical effort to understand these materials has been hampered by the lack of effective computational algorithms. Such algorithms are essential in order to solve model systems to compare with experiment and guide the selection of analytic approximations. Among such model systems the Hubbard model, believed to contain the gross features of electron correlations in high temperature superconductors, has received an unprecedented level of attention. Yet many basic questions remain open on its properties. By developing new algorithms for scalable high-performance computing, we will perform accurate computations of electron correlations in this and related models. The objective in education is to incorporate computing into the curriculum in a seamless way. High-performance computing impacts more than research and presents enormous opportunities. To fully take advantage, students must be adequately prepared with skills in computation. By curriculum development, outreach and mentoring, and cross- disciplinary collaboration, the gap in computational science in the William and Mary curriculum will be filled and students will be involved in state-of-the-art computational physics research. %%% This is a CAREER award which integrates research and education in computational materials physics. The research will address long-standing questions on electron correlations in high temperature superconductors. Intense theoretical effort to understand these materials has been hampered by the lack of effective computational algorithms. Such algorithms are essential in order to solve model systems to compare with experiment and guide the selection of analytic approximations. Among such model systems the Hubbard model, believed to contai n the gross features of electron correlations in high temperature superconductors, has received an unprecedented level of attention. Yet many basic questions remain open on its properties. By developing new algorithms for scalable high-performance computing, we will perform accurate computations of electron correlations in this and related models. The objective in education is to incorporate computing into the curriculum in a seamless way. High-performance computing impacts more than research and presents enormous opportunities. To fully take advantage, students must be adequately prepared with skills in computation. By curriculum development, outreach and mentoring, and cross- disciplinary collaboration, the gap in computational science in the William and Mary curriculum will be filled and students will be involved in state-of-the-art computational physics research. ***

9734041 Zhang这是一个CAREER奖，它将计算材料物理的研究和教育结合在一起。 这项研究将解决高温超导体中电子相关性的长期问题。 由于缺乏有效的计算算法，理解这些材料的密集理论努力受到阻碍。 这样的算法是必不可少的，以解决模型系统与实验比较，并指导选择解析近似。 在这些模型系统中，哈伯德模型被认为包含了高温超导体中电子关联的总体特征，受到了前所未有的关注。 然而，关于它的性质，许多基本问题仍然悬而未决。 通过开发可扩展的高性能计算的新算法，我们将在此模型和相关模型中执行电子相关性的精确计算。 教育的目标是以无缝的方式将计算纳入课程。 高性能计算的影响不仅限于研究，还带来了巨大的机遇。 为了充分利用这一优势，学生必须具备充分的计算技能。 通过课程开发、推广和指导以及跨学科合作，威廉和玛丽课程中计算科学的差距将得到填补，学生将参与最先进的计算物理研究。 %%% 这是一个职业奖，它整合了计算材料物理学的研究和教育。 这项研究将解决高温超导体中电子相关性的长期问题。 由于缺乏有效的计算算法，理解这些材料的密集理论努力受到阻碍。 这样的算法是必不可少的，以解决模型系统与实验比较，并指导选择解析近似。 在这些模型体系中，Hubbard模型被认为包含了高温超导体中电子关联的总体特征，受到了前所未有的关注。 然而，关于它的性质，许多基本问题仍然悬而未决。 通过开发可扩展的高性能计算的新算法，我们将在此模型和相关模型中执行电子相关性的精确计算。 教育的目标是以无缝的方式将计算纳入课程。 高性能计算的影响不仅限于研究，还带来了巨大的机遇。 为了充分利用这一优势，学生必须具备充分的计算技能。 通过课程开发、推广和指导以及跨学科合作，威廉和玛丽课程中计算科学的差距将得到填补，学生将参与最先进的计算物理研究。 ***