Low Cost Generalized Coupled Cluster Theory for Static and Dynamic Correlations

莱斯大学的Gustavo Scuseria获得了化学系化学理论，模型和计算方法项目的奖项，以开发新颖，有效和准确的方法来处理电子运动强烈相关的分子和材料。 计算方法在理解化学和物理行为方面发挥着关键作用，因此可以帮助指导依赖于新材料特性的变革性技术的发展。 然而，存在现有技术不足以解决的重要问题。 最突出的是，目前的计算工具不能正确地描述强相关性，这是负责超导性和各种重要的磁现象。 本研究试图通过在一个新的和独特的方向扩展传统的方法来弥补这一不足。 这些新方法将被应用于研究石墨系统中的强关联，这是显示磁有序系统的原型。 从长远来看，该提案中开发的工具也将对化学，物理和材料科学的其他研究人员有用。 一组不同的研究生和博士后同事参与了这项研究。为了实现这些目标，Scuseria和同事联合收割机结合最近开发的对耦合集群理论（这是能够描述强静态相关）与李代数相似性变换（这是能够描述其余的弱相关性需要达到定量的准确性）。 这两种技术的结合应该以合理的代价描述强相关和弱相关，同时避免非物理对称性破缺，而非物理对称性破缺是更传统的方法所需要的，但这使它们的预测有点可疑。 富勒烯，石墨烯纳米带和纳米点的应用将展示这种新的计算方法的能力，并将有助于解释其独特的性能。

Gustavo Scuseria of Rice University is supported by an award from the Chemical Theory, Models and Computational methods program in the Chemistry Division to develop novel, efficient and accurate methods to treat molecules and materials in which the motion of electrons is strongly correlated. Computational methods play a key role in understanding chemical and physical behavior and therefore can help guide the development of transformative technologies which rely on novel material properties. However, there are important problems for which the existing techniques are inadequate. Most prominently, current computational tools do not properly describe the strong correlations which are responsible for superconductivity and various important magnetic phenomena. This research seeks to remedy that deficiency by extending conventional methods in a new and unique direction. These new methods will be applied to study the strong correlations in graphitic systems, which serve as prototypes for systems displaying magnetic ordering. In the longer term, the tools developed in this proposal will be useful for other researchers in chemistry, physics, and materials science as well. A diverse group of graduate students and postdoctoral associates are involved in carrying out this research. In order to accomplish these goals, Scuseria and coworkers combine the recently developed pair coupled cluster theory (which is able to describe strong static correlations) with Lie algebraic similarity transformation (which is able to describe the remaining weak correlations needed to reach quantitative accuracy). The combination of these two techniques should describe both strong and weak correlations with reasonable expense while avoiding the unphysical symmetry breaking which more conventional methods require but which makes their predictions somewhat suspect. The applications to fullerenes, graphene nanoribbons, and nanodots will demonstrate the capabilities of this new computational approach and will help explain their unique properties.