Strongly Correlated Fermi Systems

强相关费米系统

• 批准号: 1733071
• 负责人: Gabriel Kotliar
• 金额: $ 45万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1733071&HistoricalAwards=false
• 关键词: Strongly; Correlated; Fermi; Systems

NONTECHNICAL SUMMARY This award supports theoretical and computational research and education to advance understanding of strongly correlated materials which have unusual properties and to improve their theoretical description. Materials are ubiquitous in our daily life and improving them is key for future technological progress. This project advances the methods, concepts, techniques and computer codes needed to understand and predict the properties of strongly correlated electron materials. In these solids, the strong interactions among electrons lead to correlations in their motions that are challenging to describe in standard theoretical frameworks. Strongly correlated electron materials display a wealth of new and unusual physical properties, ranging from superconductivity at unusually high temperature to materials that transform from metals to insulators, a process that depends sensitively on external conditions. Superconductivity enables the flow of electric currents without resistance, while metal to insulator transitions are used in fast switches and new memory devices. A goal is to advance the predictive power of the theory to accelerate the process of material discovery and design.This project enables multiple educational activities, such as the training of undergraduate and graduate students, as well as postdoctoral associates, in the use of analytical and computational methods, and the use of computational facilities. Research will be disseminated through journal publications and the internet, as well as through seminars, conferences and schools. TECHNICAL SUMMARY This award supports theoretical and computational research and education to advance understanding of strongly correlated materials and to improve their theoretical description. This award supports developing methods, concepts, theory, algorithms and computer codes to understand the physical properties of strongly correlated materials in and out of equilibrium. A long-term goal is to enhance predictive power to accelerate the process of material discovery and design utilizing strongly correlated materials. Simplified many-body Hamiltonians will be constructed and used to understand qualitative aspects of strong correlation phenomena at low energies with computationally intensive calculations that model more accurately the microscopic complexity of real materials. Computational methods include combining electronic structure and dynamical mean-field theory. This project will continue the development and testing of this non-perturbative approach on materials of current experimental interest. In materials proximate to the Mott transition, the physics is governed by charge blocking. The PI will explore the implications of strong correlations near a Mott transition in nonequilibrium steady states in the presence of dissipation and electric fields. In Hund's metals, the physics is governed by spin blocking while the charge fluctuates strongly. The PI aims to develop the theory of Hund's metal and apply it to prototypical systems such as the iron pnictides and chalcogenide high-temperature superconductors and the ruthenium oxides. This project enables multiple educational activities, such as the training of undergraduate and graduate students, as well as postdoctoral associates, in the use of analytical and computational methods, and the use of computational facilities. Research will be disseminated through journal publications and the internet, as well as through seminars, conferences and schools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项支持理论和计算研究和教育，以促进对具有不寻常性质的强相关材料的理解，并改进其理论描述。材料在我们的日常生活中无处不在，改善它们是未来技术进步的关键。该项目推进了理解和预测强相关电子材料性质所需的方法、概念、技术和计算机代码。在这些固体中，电子之间的强相互作用导致了它们运动中的相关性，这在标准理论框架中很难描述。强相关电子材料显示出大量新的和不寻常的物理特性，从异常高温下的超导性到从金属转变为绝缘体的材料，这一过程对外部条件非常敏感。超导性使电流无电阻流动，而金属到绝缘体的转换用于快速开关和新的存储设备。目标是提高理论的预测能力，以加速材料发现和设计的过程。该项目支持多种教育活动，如培训本科生和研究生以及博士后，使用分析和计算方法，以及使用计算设施。研究将通过期刊出版物和互联网，以及通过研讨会、会议和学校传播。该奖项支持理论和计算研究和教育，以促进对强相关材料的理解，并改进其理论描述。该奖项支持开发方法、概念、理论、算法和计算机代码，以了解处于和处于平衡状态的强相关材料的物理特性。长期目标是提高预测能力，以加速利用强相关材料的材料发现和设计过程。简化的多体哈密顿量将被构建并用于理解低能强相关现象的定性方面，通过计算密集的计算，更准确地模拟真实材料的微观复杂性。计算方法包括结合电子结构和动力学平均场理论。该项目将继续在当前实验感兴趣的材料上开发和测试这种非扰动方法。在接近莫特跃迁的材料中，物理是由电荷阻塞控制的。PI将探索在耗散和电场存在的非平衡稳态中莫特跃迁附近的强相关性的含义。在洪德的金属中，当电荷剧烈波动时，物理是由自旋阻塞控制的。PI的目标是发展亨德的金属理论，并将其应用于原型系统，如铁衍生物和硫系高温超导体以及氧化钌。该项目支持多种教育活动，如培训本科生和研究生以及博士后，使用分析和计算方法，以及使用计算设施。研究将通过期刊出版物和互联网，以及通过研讨会、会议和学校传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Nonequilibrium mean-field theory of resistive phase transitions

• DOI: 10.1103/physrevb.98.035145
• 发表时间: 2018-07-27
• 期刊: PHYSICAL REVIEW B
• 影响因子: 3.7
• 作者: Han, Jong E.;Li, Jiajun;Kotliar, Gabriel
• 通讯作者: Kotliar, Gabriel

Steady-state superconductivity in electronic materials with repulsive interactions

具有排斥相互作用的电子材料的稳态超导性

• DOI: 10.1103/physrevb.100.060508
• 发表时间: 2019
• 期刊: Physical Review B
• 影响因子: 3.7
• 作者: Hart O

Antiferromagnetic Order Breaks Inversion Symmetry in a Metallic Double Perovskite, Pb 2 NiOsO 6

反铁磁有序打破金属双钙钛矿 Pb 2 NiOsO 6 中的反演对称性

• DOI: 10.1021/acs.chemmater.1c01032
• 发表时间: 2021
• 期刊: Chemistry of Materials
• 影响因子: 8.6
• 作者: Feng, Hai L.;Kang, Chang-Jong;Manuel, Pascal;Orlandi, Fabio;Su, Yu;Chen, Jie;Tsujimoto, Yoshihiro;Hadermann, Joke;Kotliar, Gabriel;Yamaura, Kazunari
• 通讯作者: Yamaura, Kazunari

Signatures of Mottness and Hundness in archetypal correlated metals

• DOI: 10.1038/s41467-019-10257-2
• 发表时间: 2019-06-20
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Deng, Xiaoyu;Stadler, Katharina M.;Kotliar, Gabriel
• 通讯作者: Kotliar, Gabriel

Orbital differentiation in Hund metals

• DOI: 10.1103/physrevb.100.115159
• 发表时间: 2019-09-27
• 期刊: PHYSICAL REVIEW B
• 影响因子: 3.7
• 作者: Kugler, Fabian B.;Lee, Seung-Sup B.;von Delft, Jan
• 通讯作者: von Delft, Jan