Computational Quantum Condensed Matter

计算量子凝聚态物质

• 批准号: 250899-2012
• 负责人: Sorensen, Erik
• 金额: $ 2.99万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570933
• 关键词: Computational; Quantum; Condensed; Matter

The objective of this proposal is to apply state of the art numerical techniques to simulate new materials, in particular materials where so called strong correlation effects or frustration are important. Many materials display frustration and strong correlation effects giving rise to novel phases and properties that is currently the focus of intense study. In essence, what we mean by this is that the system as a whole possess properties that the individual parts do not have. When we model such materials it becomes apparent that analytical calculations are difficult and often unreliable and the only viable alternative is to perform large scale simulations using computers. Here it is proposed to apply this approach to problems where such strong correlations and frustration are important, such as quantum spin chains, frustrated magnets, supercondcutivity and other strongly correlated or disordered systems. In particular it is proposed to investigate spin liquid and other exotic phases as they occur in these systems as well as the transition into these novel phases. An aspect of special importance is the entanglement, a measure of the inherent quantum mechanical nature of these materials at low temperatures. A secondary objective is to develop new advanced numerical algorithms for the study of these materials. A detailed understanding of entanglement has proven crucial for this objective. The availability of numerical results would be of considerable importance for the development of new materials vital to our scientific competitiveness and the development of new numerical techniques might improve our understanding or directly solve grand-challenge computational problems, such as the sign problem, and could lead to new parallel programming paradigms.

该提案的目的是应用最先进的数值技术来模拟新材料，特别是所谓的强相关效应或挫折很重要的材料。 许多材料表现出挫败感和强烈的相关效应，从而产生新的相和特性，这是目前深入研究的焦点。从本质上讲，我们的意思是系统作为一个整体拥有各个部分所不具备的属性。当我们对此类材料进行建模时，很明显分析计算很困难而且通常不可靠，唯一可行的替代方案是使用计算机进行大规模模拟。 这里建议将这种方法应用于强相关性和挫败性很重要的问题，例如量子自旋链、挫败磁体、超导性和其他强相关或无序系统。特别是，建议研究这些系统中出现的自旋液体和其他奇异相以及向这些新相的转变。特别重要的一个方面是纠缠，这是衡量这些材料在低温下固有量子力学性质的指标。 第二个目标是开发新的先进数值算法来研究这些材料。 事实证明，对纠缠的详细理解对于实现这一目标至关重要。 数值结果的可用性对于开发对我们的科学竞争力至关重要的新材料非常重要，新数值技术的开发可能会提高我们的理解或直接解决具有挑战性的计算问题，例如符号问题，并可能导致新的并行编程范例。