Collaborative Research: Worm Algorithm and Diagrammatic Monte Carlo in Atomic and Condensed Matter Physics

合作研究：原子和凝聚态物理中的蠕虫算法和图解蒙特卡罗

• 批准号: 1314735
• 负责人: Boris Svistunov
• 金额: $ 87万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1314735&HistoricalAwards=false
• 关键词: Collaborative; Research; Worm; Algorithm; Diagrammatic

This project focuses on strongly correlated phases of cold-atomic systems in optical lattices where collective behavior is governed by laws of quantum mechanics. The prototypical strongly correlated fermionic systems -- the Hubbard model and resonant fermions in the regime of BCS-BEC crossover -- are central in the fields of the cold atom research and condensed matter. Understanding quantum plasticity and super transport in solid 4He remains one of big challenges in modern low-temperature physics. There is urgent need for universal unbiased first-principles methods for strongly correlated fermionic systems across all fields of physics, quantum chemistry, and materials science. Ab initio and model simulations provide crucial information about quantitative and qualitative properties of these systems, test analytical predictions and help establish the proper theoretical framework, lay the ground for the unambiguous analysis of experimental data and further development of measuring techniques. In particular, this project aims to (i) develop generic diagrammatic Monte Carlo (DiagMC) tools for continuous-space and lattice fermionic/fermionized systems: resonant fermions, Hubbard model, and frustrated spin systems, (ii) understand quantum plasticity and supertransport in solid helium-4, (iii) perform Worm Algorithm (WA) studies of quantum-critical phenomena and novel phases of ultra-cold atoms: deconfined criticality, universal critical dynamics, phases of multi-bound complexes of polar molecules.An unbiased theoretical description of collective quantum phenomena is of vital interdisciplinary importance for a number of applied and fundamental areas, such as quantum computing and high-energy physics. High-end computing methods and techniques often find applications outside the physics community. Simulations of complex models with multiple constraints, randomness, and a variable number of continuous parameters are typical in polymer science, neural networks, computer science, behavioral, social and economics studies. The algorithms developed in the project provide an example of how some of the difficulties may be circumvented. An integral part of the project is the training of graduate students and post-doctoral associate in advanced numeric techniques, quantum statistics, topical problems of atomic and solid state physics, network administration, and parallel supercomputing.

该项目重点研究光学晶格中冷原子系统的强相关相，其中集体行为受量子力学定律支配。典型的强相关费米子系统——BCS-BEC交叉体系中的哈伯德模型和共振费米子——是冷原子研究和凝聚态物质领域的核心。了解固体 4He 中的量子可塑性和超输运仍然是现代低温物理学的重大挑战之一。物理学、量子化学和材料科学所有领域迫切需要针对强相关费米子系统的通用无偏第一原理方法。从头算和模型模拟提供了有关这些系统的定量和定性特性的重要信息，测试分析预测并帮助建立适当的理论框架，为实验数据的明确分析和测量技术的进一步发展奠定基础。特别是，该项目旨在 (i) 开发用于连续空间和晶格费米子/费米子化系统的通用图解蒙特卡罗 (DiagMC) 工具：共振费米子、哈伯德模型和受挫自旋系统，(ii) 了解量子可塑性和超输运固体氦 4，(iii) 对超冷原子的量子临界现象和新相进行蠕虫算法 (WA) 研究：解除临界性、通用临界动力学、集体量子现象的公正的理论描述对于许多应用和基础领域（例如量子计算和高能物理）具有至关重要的跨学科重要性。高端计算方法和技术经常在物理界之外找到应用。具有多重约束、随机性和可变数量连续参数的复杂模型的模拟在聚合物科学、神经网络、计算机科学、行为、社会和经济学研究中很常见。该项目中开发的算法提供了如何规避一些困难的示例。该项目的一个组成部分是对研究生和博士后进行高级数值技术、量子统计、原子和固体物理热点问题、网络管理和并行超级计算方面的培训。