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

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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. ***