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

9734041张，这是一个集计算材料物理研究和教育于一体的职业奖项。这项研究将解决高温超导体中电子关联的长期问题。由于缺乏有效的计算算法，理解这些材料的紧张理论努力一直受到阻碍。这样的算法对于求解模型系统以与实验进行比较并指导解析近似的选择是必不可少的。在这些模型系统中，被认为包含高温超导体中电子关联的总体特征的Hubbard模型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，我们将在该模型和相关模型中执行电子关联的准确计算。教育的目标是以一种无缝的方式将计算机纳入课程。高性能计算的影响不仅仅是研究，还带来了巨大的机遇。为了充分利用这一优势，学生必须做好充分的计算技能准备。通过课程开发、外展和指导以及跨学科合作，威廉和玛丽课程中计算科学的空白将被填补，学生将参与最先进的计算物理研究。这是一个集计算材料物理研究和教育于一体的职业奖项。这项研究将解决高温超导体中电子关联的长期问题。由于缺乏有效的计算算法，理解这些材料的紧张理论努力一直受到阻碍。这样的算法对于求解模型系统以与实验进行比较并指导解析近似的选择是必不可少的。在这些模型系统中，被认为包含高温超导体中电子关联的总体特征的Hubbard模型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，我们将在该模型和相关模型中执行电子关联的准确计算。教育的目标是以一种无缝的方式将计算机纳入课程。高性能计算的影响不仅仅是研究，还带来了巨大的机遇。为了充分利用这一优势，学生必须做好充分的计算技能准备。通过课程开发、外展和指导以及跨学科合作，威廉和玛丽课程中计算科学的空白将被填补，学生将参与最先进的计算物理研究。***

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. ***