Collaborative ITR: Computational Design of Magnetic and Superconducting Transitions Based on Cluster DMFT Approach to Electronic Structure Calculation

协作 ITR：基于电子结构计算的簇 DMFT 方法的磁和超导转变的计算设计

• 批准号: 0606498
• 负责人: Sergey Savrasov
• 金额: $ 26.4万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0606498&HistoricalAwards=false
• 关键词: Collaborative; ITR; Computational; Design; Magnetic

TECHNICAL SUMMARY:This award supports collaborative computational and theoretical research at the University of California at Davis and Rutgers University through #0606096. The PIs aim to develop a robust computational approach to study, design, and visualize properties of materials containing strongly correlated electrons and showing magnetism, superconductivity, and other possibly novel ordered states. Novel information technology tools will be developed which will facilitate the search for new strongly correlated electron materials. This project contributes to the cyberinfrastructure of the broader materials research community.The dynamical mean field theory (DMFT) and its generalization to clusters in combinations with the theoretical methods of electronic structure calculations will be key theoretical methods employed in the research. In the course of the research, the PIs will continue to contribute to these theoretical foundations.The PIs plan to extend, enhance, and further develop the Material Information and Design Laboratory (MINDLab), a software tool for virtual material exploration created by the PIs with support from an Information Technology Research award. The PIs objectives are: i) to develop a new generation of computationally fast and robust methods, algorithms and computer codes to predict properties of strongly correlated electron materials based on a novel cluster dynamical mean field approach coupled to the electronic structure theory of materials; ii) to test and apply MINDLab by tackling frontier problems, including the search for new magnetic and superconducting materials with predictable critical temperatures, for new multiferroic materials with stronger couplings between magnetic and electric degrees of freedom, and for tunable optical materials; iii) to develop a new generation of user friendly interfaces and visualization software, as well as databases which will systematize the PIs' findings in a web integrated and searchable form; iv) to interact with experimental groups working on materials synthesis, so that the computationally created and optimized materials can be synthesized in practice. MINDLab will be made accessible via the Internet for public use by the scientific community. A simplified intuitive interface coupled with visualization techniques will enable its use as a learning tool for concepts of condensed-matter physics. NON-TECHNICAL SUMMARY:This award supports collaborative computational and theoretical research at the University of California at Davis and Rutgers University through #0606096. The PIs aim to develop a robust computational approach to study, design, and visualize properties of an intriguing class of materials, known as strongly correlated electron materials. These materials have unusual physical properties, such as high magnetic and superconducting transition temperatures, giant dielectric constants, and enhanced thermoelectric and optical properties, that reflect new or poorly understood electronic states of matter. Understanding these materials is a great intellectual challenge; the endeavor holds promise for many potential new discoveries. The PI's will develop computational and visualization tools, collectively called Material Information and Design Laboratory (MINDLab), that are based on promising recent theoretical advances. They will use them to study strongly correlated materials, to attempt to predict their properties, and to contribute guidance to the synthesis of new materials. The PIs will make these tools available for use by the broader materials research community, contributing to its cyberinfrastructure. A simplified intuitive interface coupled with visualization techniques will enable its use as a learning tool for concepts of condensed-matter physics.

技术摘要：该奖项通过#0606096 支持加州大学戴维斯分校和罗格斯大学的协作计算和理论研究。 PI 旨在开发一种强大的计算方法来研究、设计和可视化包含强相关电子并显示磁性、超导性和其他可能新颖的有序状态的材料的特性。将开发新的信息技术工具，这将有助于寻找新的强相关电子材料。该项目为更广泛的材料研究界的网络基础设施做出了贡献。动态平均场理论（DMFT）及其对团簇的推广与电子结构计算的理论方法相结合将是该研究中采用的关键理论方法。在研究过程中，PI将继续为这些理论基础做出贡献。PI计划扩展、增强和进一步开发材料信息与设计实验室（MINDLab），这是PI创建的用于虚拟材料探索的软件工具在信息技术研究奖的支持下。 PI 的目标是： i) 开发新一代计算快速且稳健的方法、算法和计算机代码，以基于与材料电子结构理论相结合的新型簇动态平均场方法来预测强相关电子材料的特性； ii) 通过解决前沿问题来测试和应用 MINDLab，包括寻找具有可预测临界温度的新型磁性和超导材料、磁自由度和电自由度之间具有更强耦合的新型多铁材料以及可调谐光学材料； iii) 开发新一代用户友好界面和可视化软件以及数据库，以网络集成和可搜索的形式将 PI 的发现系统化； iv) 与从事材料合成的实验小组进行互动，以便可以在实践中合成通过计算创建和优化的材料。 MINDLab 将通过互联网供科学界公众使用。简化的直观界面与可视化技术相结合，使其能够用作凝聚态物理概念的学习工具。非技术摘要：该奖项通过#0606096 支持加州大学戴维斯分校和罗格斯大学的协作计算和理论研究。 PI 旨在开发一种强大的计算方法来研究、设计和可视化一类有趣的材料（称为强相关电子材料）的特性。这些材料具有不寻常的物理性质，例如高磁性和超导转变温度、巨大的介电常数以及增强的热电和光学性质，反映了新的或知之甚少的物质电子状态。 理解这些材料是一项巨大的智力挑战；这项努力有望带来许多潜在的新发现。 PI 将开发计算和可视化工具，统称为材料信息和设计实验室 (MINDLab)，这些工具基于最近有希望的理论进展。他们将利用它们来研究强相关材料，尝试预测它们的特性，并为新材料的合成提供指导。 PI 将使这些工具可供更广泛的材料研究界使用，为其网络基础设施做出贡献。简化的直观界面与可视化技术相结合，使其能够用作凝聚态物理概念的学习工具。