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Collaborative Research: Worm Algorithm and Diagrammatic Monte Carlo for strongly correlated condensed matter systems

合作研究：强相关凝聚态系统的蠕虫算法和图解蒙特卡罗

基本信息

批准号：
1720251
负责人：
Anatoly Kuklov
金额：
$ 20.83万
依托单位：
CUNY College of Staten Island
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1720251&HistoricalAwards=false
关键词：
Collaborative Research Worm Algorithm Diagrammatic

项目摘要

NONTECHNICAL SUMMARYThis award supports collaborative research and education on the collective quantum mechanical behavior of electrons in materials and of solid helium-4. The project is using and further developing two state-of-the-art computational approaches suitable for the study of quantum mechanical systems consisting of many interacting particles, the Worm Algorithm (WA) and Diagrammatic Monte Carlo (DiagMC), which were both introduced by the research team. With WA the team expects to advance understanding of striking properties demonstrated by imperfect crystals of helium-4 at low temperatures near the absolute zero of temperature, such as the frictionless transport of helium-4 atoms through the crystal, called supertransport, and an almost liquid-like response to an arbitrarily weak stress, called quantum plasticity. With DiagMC the team will address certain notoriously difficult problems concerning the behavior of many-electron systems, including the problem of how electrons develop the cooperative quantum mechanical state to become superconductors. Superconductors can conduct electricity without resistance.Understanding quantum plasticity, supertransport, and the interplay between them in solid helium-4 is a major challenge for modern low-temperature physics. More generally, there is an urgent need for universal methods suitable for describing the collective quantum behavior of electrons across all fields of physics, quantum chemistry, and materials science. The simulations at the core of the project provide crucial information about quantitative and qualitative properties of these systems, test analytical predictions, help establish the proper theoretical framework, and provide foundation for the unambiguous analysis of experimental data and the further development of measuring techniques.An integral part of the project is the training of graduate students in advanced theoretical and numerical techniques, as well as in parallel computing. The project involves developing and maintaining a tutorial website on the numerical methods used, and the PIs plan to edit a book on the same, targeting a broad scientific audience. TECHNICAL SUMMARYThis award supports collaborative research and education on the collective quantum behavior of electrons in materials and of solid helium-4. The PIs will use and further develop two state-of-the-art Monte Carlo methods introduced by the research team: the Worm Algorithm (WA), and Diagrammatic Monte Carlo (DiagMC). The main goals of the project are: i) to use DiagMC for studying notoriously difficult condensed-matter problems such as: the Cooper instability in the fermionic repulsive Hubbard model including the possibility of high critical temperatures, modeling electronic systems with controlled ab initio treatment of long-range Coulomb and electron-phonon interactions, creating alternative formulations for strongly correlated models, and understanding the quantum-to-classical correspondence in frustrated spin models; ii) to carry out WA studies of disorder-induced quantum physics in solid He-4, such as supertransport and quantum plasticity associated with generic (tilted) dislocations.Understanding quantum plasticity, supertransport, and the interplay between them in solid helium-4 is a major challenge for modern low-temperature physics. More generally, there is an urgent need for universal methods suitable for strongly correlated fermionic systems across all fields of physics, quantum chemistry, and materials science. The simulations at the core of the project provide crucial information about quantitative and qualitative properties of these systems, test analytical predictions, help establish the proper theoretical framework, and provide foundation for the unambiguous analysis of experimental data and the further development of measuring techniques.An integral part of the project is the training of graduate students in advanced theoretical and numerical techniques, as well as in parallel computing. The project involves developing and maintaining a tutorial website on the numerical methods used, and the PIs plan to edit a book on the same, targeting a broad scientific audience.

非技术总结该奖项支持关于材料中电子和固体氦-4的集体量子力学行为的合作研究和教育。该项目正在使用并进一步开发两种适用于研究由许多相互作用的粒子组成的量子力学系统的最先进的计算方法，Worm算法(WA)和图解蒙特卡罗(DiagMC)，这两种方法都是由研究团队介绍的。有了WA，该团队希望推进对氦-4不完美晶体在绝对零度附近的低温下所表现出的惊人特性的理解，例如氦-4原子在晶体中的无摩擦传输，称为超级传输，以及对任意弱应力的近乎液态的响应，称为量子可塑性。有了DiagMC，该团队将解决一些关于多电子系统行为的臭名昭著的难题，包括电子如何发展合作量子力学状态成为超导体的问题。超导体可以无电阻地导电。了解固体氦-4中的量子塑性、超输运以及它们之间的相互作用是现代低温物理学面临的重大挑战。更广泛地说，迫切需要通用的方法来描述物理、量子化学和材料科学中所有领域的电子的集体量子行为。该项目的核心模拟提供了有关这些系统的定量和定性性质的关键信息，测试分析预测，帮助建立适当的理论框架，并为实验数据的明确分析和测量技术的进一步发展提供基础。该项目的一个组成部分是对研究生进行高级理论和数值技术以及并行计算方面的培训。该项目涉及开发和维护一个关于所使用的数值方法的教程网站，国际和平研究所计划在此基础上编辑一本书，面向广泛的科学受众。技术总结该奖项支持关于材料中电子和固体氦-4的集体量子行为的合作研究和教育。PIS将使用并进一步开发研究团队介绍的两种最先进的蒙特卡罗方法：蠕虫算法(WA)和图解蒙特卡罗(Diagramatic蒙特卡罗)(DiagMC)。该项目的主要目标是：i)使用DiagMC研究众所周知的困难的凝聚态问题，例如：费米子排斥Hubbard模型中的库珀不稳定性，包括高临界温度的可能性，用长程库仑和电子-声子相互作用的受控从头算处理来模拟电子系统，为强关联模型创建替代公式，以及理解受挫自旋模型中量子与经典的对应；Ii)开展He-4固体无序诱导量子物理的研究，如超输运和与一般(倾斜)位错相关的量子塑性。了解固体氦-4中的量子塑性、超输运及其相互作用是现代低温物理的主要挑战。更广泛地说，迫切需要适用于物理、量子化学和材料科学所有领域的强关联费米子系统的通用方法。该项目的核心模拟提供了有关这些系统的定量和定性性质的关键信息，测试分析预测，帮助建立适当的理论框架，并为实验数据的明确分析和测量技术的进一步发展提供基础。该项目的一个组成部分是对研究生进行高级理论和数值技术以及并行计算方面的培训。该项目涉及开发和维护一个关于所使用的数值方法的教程网站，国际和平研究所计划在此基础上编辑一本书，面向广泛的科学受众。