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RII Track-4: Big Data and Massive Computation Approaches to Non-Equilibrium Dynamics of Strongly Correlated Materials

RII Track-4：强相关材料非平衡动力学的大数据和大规模计算方法

基本信息

批准号：
1738698
负责人：
Cheng-Chien Chen
金额：
$ 22.21万
依托单位：
University of Alabama at Birmingham
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-15 至 2022-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738698&HistoricalAwards=false
关键词：
RII Track Big Data Massive

项目摘要

Non-technical DescriptionIn some materials, the motion of elections through the material structure can be highly correlated, such that the electrons behave as cars move in heavy traffic; they cannot maneuver freely and their motions are strongly influenced by others. Materials that exhibit electron correlations also exhibit intriguing properties, such as metal-insulator transitions and unconventional superconductivity. However, whether electron correlation is the sole cause of these observed effects has perplexed scientists for decades. Overcoming this gap in knowledge could open up revolutionary opportunities for novel transistor and ultrafast device applications. In this project, the PI will use the supercomputing capabilities at Oak Ridge National Laboratory to tackle this challenging problem. Advanced simulations will be performed using tens of thousands of computing processors to model the behavior of electron correlation systems at the atomic scale. The research will generate more than 100 terabytes of data, requiring that Big Data techniques be employed to effectively process and analyze the huge data volume. The project includes efforts to broaden participation in the next-generation scientific computing workforce. The research topics address several of the 10 Big Ideas for Future NSF Investments and the Grand Challenges in Basic Energy Sciences, thereby also having potential impacts on U.S. science leadership and an energy-sustainable future.Technical DescriptionThe project's focus is on modeling quantum many-body phenomena driven away from equilibrium, with the goal of understanding correlated electrons' non-equilibrium behaviors revealed by time-domain spectroscopies at ultra-short time scales. Matrix diagonalization over 100 billion basis states will be tackled by matrix-free and dataflow computing. Equilibrium and non-equilibrium wavefunction-based quantum impurity solvers also will be developed. Non-linear time series regression will be further implemented to alleviate the computational cost for time-evolution calculations. The resulting codes will be employed to simulate ultrafast photon-based spectroscopies on vanadium dioxide (VO2), using effective single-band Hubbard model and multi-orbital Hamiltonian from Wannier projection. The role of structure transition will be addressed by restricted phonon calculations. These simulations could significantly advance the understanding of non-equilibrium phenomena and photo-induced phase transitions in VO2 and other strongly correlated transition-metal oxides. Open-source softwares also will be made freely available to the public for parallel cloud computing to further benefit the scientific community for numerical studies of non-equilibrium many-body problems.

非技术描述在某些材料中，电子通过物质结构的运动可以高度相关，以至于电子的行为就像汽车在繁忙的交通中移动一样；它们不能自由移动，它们的运动受到其他人的强烈影响。表现出电子关联的材料也表现出有趣的性质，例如金属-绝缘体转变和非传统的超导电性。然而，电子相关性是否是这些观察到的效应的唯一原因，几十年来一直困扰着科学家。克服这一知识差距可能会为新型晶体管和超快器件的应用开辟革命性的机会。在这个项目中，PI将利用橡树岭国家实验室的超级计算能力来解决这个具有挑战性的问题。将使用数以万计的计算处理器进行高级模拟，以模拟原子尺度上的电子关联系统的行为。这项研究将产生超过100 TB的数据，需要采用大数据技术来有效地处理和分析海量数据。该项目包括努力扩大对下一代科学计算劳动力的参与。研究主题涉及未来NSF投资的十大想法中的几个以及基础能源科学方面的重大挑战，从而也对美国的科学领导地位和能源可持续未来产生潜在影响。技术说明该项目的重点是对偏离平衡的量子多体现象进行建模，目的是了解相关电子在超短时间尺度上的时间域谱揭示的非平衡行为。超过1000亿个基态的矩阵对角化将通过无矩阵和数据流计算来解决。还将开发基于平衡和非平衡波函数的量子杂质解算器。将进一步实现非线性时间序列回归，以减轻时间演化计算的计算成本。所生成的程序将被用于模拟基于二氧化钒(VO2)的超快光子光谱，使用有效的单带Hubbard模型和来自Wannier投影的多轨道哈密顿量。结构转变的作用将通过限制声子计算来解决。这些模拟可以极大地促进对VO2和其他强相关过渡金属氧化物中的非平衡现象和光致相变的理解。开放源码软件还将免费提供给公众进行并行云计算，以进一步造福于科学界对非平衡多体问题的数值研究。