New Numerical and Theoretical Methods to Analyse Disordered Materials

分析无序材料的新数值和理论方法

• 批准号: 0812204
• 负责人: Stefan Boettcher
• 金额: $ 24.6万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812204&HistoricalAwards=false
• 关键词: New; Numerical; Theoretical; Methods; Analyse

TECHNICAL SUMMARY:This award supports computational and theoretical research on the low-temperature properties of spin glasses and models inspired by strongly disordered materials. The research is based in part on advances made on a project supported through the Information Technology Research initiative. This project involves developing, applying, and communicating new methods to probe structural and dynamical properties of disordered systems and networks, utilizing the renormalization group and the results of research on the Extremal Optimization heuristic. Thrusts of the research include: 1) Frustrated lattices requiring up to a billion variables above and near bond percolation will be simulated to predict low-temperature exponents with the accuracy essential to reveal scaling relations and to tightly constrain theoretical models. 2) New meta-heuristics will be developed; these are indispensable for low-energy properties of glassy materials, combinatorial optimization, or complex networks. 3) Investigating statistical models at the transition between low-dimensional and mean-field behavior using numerical and analytical techniques to elucidate scaling and critical dimensions in glasses, and small-world properties in networks. The PI will explore a wide range of applications for his heuristics and new network models.This award supports the education of students, whose academic training provides the future backbone of our information technology infrastructure. The PI plans to develop sophisticated software in a web-based environment, utilizing a student-administered cluster-computer. All results and software products will be disseminated widely through publications, presentations, and the internet. The projects will provide students with education and research experience in the study of statistical physics. Ties with researchers at Los Alamos National Laboratory will provide students further real-life research experience.NONTECHNICAL SUMMARY:This award supports computational and theoretical research at the interface of statistical physics and computer science. The research is inspired by disordered materials in which atoms or the smallest units of magnetism cannot usefully be viewed as being arranged in a regular crystalline array. The models that are believed to contain essential physics of these materials are examples of complex systems known both to materials research and computer science. Included in this class of problems are how proteins find the optimum configuration of atoms for biological function, circuit design, and the seemingly simple problem of finding the minimum distance a salesman must travel to visit each city of a list of cities only once. These are difficult to solve as they contain conflicting constraints and are characterized by many almost correct solutions. The PI has developed a computer algorithm that may elucidate the properties of disordered magnetic materials at low-temperatures. This award supports a continuation of that work and the more general application of the PI?s computer algorithm to a wider class of problems known as optimization problems. The research is based in part on advances made on a project supported through the Information Technology Research initiative. This award supports the education of students, whose academic training provides the future backbone of our information technology infrastructure. The PI plans to develop sophisticated software in a web-based environment, utilizing a student-administered cluster-computer. All results and software products will be disseminated widely through publications, presentations, and the internet. The projects will provide students with education and research experience in the study of statistical physics. Ties with researchers at Los Alamos National Laboratory will provide students further real-life research experience.

技术摘要：该奖项支持对自旋玻璃的低温特性和受强无序材料启发的模型的计算和理论研究。这项研究部分基于信息技术研究倡议支持的一个项目所取得的进展。该项目涉及开发，应用和交流新方法来探索无序系统和网络的结构和动力学特性，利用重整化群和极值优化启发式研究结果。该研究的重点包括：1）将模拟需要高达10亿个变量以上和附近的键渗流的挫折晶格，以预测低温指数，其准确性对于揭示标度关系和严格约束理论模型至关重要。2)新的元动力学将被开发;这些是必不可少的低能量性质的玻璃材料，组合优化，或复杂的网络。3)利用数值和分析技术研究低维和平均场行为之间过渡的统计模型，以阐明玻璃中的标度和临界尺寸，以及网络中的小世界特性。PI将探索其物流和新网络模型的广泛应用。该奖项支持学生的教育，其学术培训为我们的信息技术基础设施提供了未来的支柱。PI计划在基于网络的环境中开发复杂的软件，利用学生管理的集群计算机。所有成果和软件产品都将通过出版物、介绍和互联网广泛传播。这些项目将为学生提供统计物理学研究方面的教育和研究经验。与洛斯阿拉莫斯国家实验室的研究人员的联系将为学生提供进一步的现实生活中的研究经验。非技术性总结：该奖项支持统计物理和计算机科学接口的计算和理论研究。这项研究的灵感来自于无序材料，其中原子或最小的磁性单元不能有效地被视为以规则的晶体阵列排列。被认为包含这些材料的基本物理特性的模型是材料研究和计算机科学已知的复杂系统的例子。这类问题包括蛋白质如何为生物功能、电路设计找到原子的最佳配置，以及找到推销员必须旅行一次才能访问城市列表中的每个城市的最小距离。这些问题很难解决，因为它们包含相互冲突的约束，并且具有许多几乎正确的解决方案。PI开发了一种计算机算法，可以阐明无序磁性材料在低温下的特性。该奖项支持继续这项工作和更广泛的应用PI？的计算机算法，以更广泛的一类问题，称为优化问题。 这项研究部分基于信息技术研究倡议支持的一个项目所取得的进展。该奖项支持学生的教育，他们的学术培训提供了我们的信息技术基础设施的未来骨干。PI计划在基于网络的环境中开发复杂的软件，利用学生管理的集群计算机。所有成果和软件产品都将通过出版物、介绍和互联网广泛传播。这些项目将为学生提供统计物理学研究方面的教育和研究经验。与洛斯阿拉莫斯国家实验室的研究人员的联系将为学生提供进一步的现实生活中的研究经验。