CMMT: Slave-boson approach for electronically correlated metal oxides

该奖项支持旨在开发和应用准确和计算效率高的方法的研究，这些方法可以模拟电子相互作用强烈的材料的特性。系统地改变材料特性的能力是技术进步的基础，有几个众所周知的例子，如硅基半导体的工程设计，以创建电子设备或大规模生产日常照明用的节能发光半导体二极管。在这些半导体材料中，负责关键材料特性的电子运动相对容易理解和预测，因为电子相互作用相当弱。然而，还有许多其他有趣和潜在有用的特性，如磁性，高温超导性，或在电子强烈相互作用的材料中发生的强热电响应。理解和计算这些材料的性质对于基础科学和潜在的应用是有意义的，但由于电子之间的强相互作用，很难做到这一点。在这个项目中，PI和他的团队将开发和应用新的计算方法来克服这一挑战。他们将对已知材料的预测进行基准测试，并预测新材料的性能，然后通过实验进行验证。该项目开发的方法将通过出版物、会议演示和通过现有开源数据库发布的开源公共软件分发给更广泛的科学界。该奖项支持在计算材料物理学的研究生和博士后研究助理的培训。为了推广，研究团队将与参加纽黑文科学博览会的几名K-12学生接触，首先帮助他们改进科学博览会项目的实验设计，技术总结该奖项支持旨在开发和应用描述固体物理性质的理论和计算方法的研究，状态金属氧化物材料，其中材料中电子之间的强相互作用导致复杂且潜在有用的行为，例如磁性、高温超导性和大的热电响应。了解和预测电子强烈相互作用的材料的性质是一项艰巨的挑战，也是物理、化学和材料科学界长期感兴趣的主题。在这个项目中，研究小组将采用从玻色子框架，其中困难的相互作用电子问题是通过使用辅助（也称为辅助或从）玻色子粒子来帮助以计算上易于处理的方式模拟电子相互作用。在这个项目中使用的方法包括多体理论，量子力学的费米子和玻色子系统，和数值算法解决大型量子力学问题。 一个关键的研究推力涉及到一个相互作用的集群方法，超越了传统的单一相互作用的网站技术，以提高从玻色子方法的准确性的发展。这些方法将以广为人知的材料为基准，以评估其与竞争方法相比的优缺点。这些方法也将被用于理解和预测尖端材料的性质，这些材料以金属氧化物超晶格和薄膜的形式存在，在不久的将来可以通过实验制造和测量。在这个项目中开发的方法将通过出版物，会议演示和通过现有的开源数据库发布的开源公共软件发布到更广泛的科学界。该奖项支持在计算材料物理学的研究生和博士后研究助理的培训。为了推广，研究团队将与参加纽黑文科学博览会的几名K-12学生接触，首先帮助他们改进科学博览会项目的实验设计，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。

NONTECHNICAL SUMMARYThis award supports research aimed at developing and applying accurate and computationally efficient methods that can model the properties of materials in which electrons interact strongly with each other. The ability to systematically modify the properties of materials underlies technological progress, with several well-known examples such as the engineering of silicon-based semiconductors to create electronic devices or the mass production of energy-efficient light-emitting semiconducting diodes used daily for illumination. In these semiconducting materials, the electronic motions that are responsible for the key materials properties are relatively easy to understand and predict because the electrons interact rather weakly with each other. However, there are many other interesting and potentially useful properties such as magnetism, high-temperature superconductivity, or strong thermoelectric response that occur in materials where electrons interact strongly. Understanding and calculating the properties of such materials is of interest for basic science and potential applications, but it is difficult to do so due to the strong interactions among the electrons. In this project, the PI and his team will develop and apply new computational methods to overcome this challenge. They will benchmark their predictions for well-known materials and predict properties of new materials that can then be verified by experiments. The methods developed in this project will be distributed to the broader scientific community via publications, conference presentations, and open-source public software releases via an existing open-source database. The award supports the training of graduate students and a postdoctoral research associate in computational materials physics. For outreach, the research team will engage with several K-12 students participating in the New Haven Science Fair by initially helping them improve their experimental designs for their Science Fair projects, and later by helping via further mentoring and judging in the Science Fair itself.TECHNICAL SUMMARYThis award supports research aimed at developing and applying theoretical and computational methods that describe the physical properties of solid-state metal oxide materials where the strong interactions between the electrons in the materials lead to complex and potentially useful behaviors such as magnetism, high-temperature superconductivity, and large thermoelectric responses. Understanding and predicting the properties of materials where electrons interact strongly is a difficult grand challenge and a long-standing subject of interest for the physics, chemistry, and materials science communities. In this project, the research team will employ the slave-boson framework, in which the difficult interacting electron problem is approached by using auxiliary (also called subsidiary or slave) boson particles to help model the electronic interactions in a computationally tractable manner. The methodologies used in this project include many-body theory, quantum mechanics for fermionic and bosonic systems, and numerical algorithms for solution of large quantum mechanical problems. One key research thrust involves the development of an interacting cluster approach that goes beyond the conventional single interacting site technique to increase the accuracy of the slave-boson method. The methodologies will be benchmarked on well-understood materials to assess their strengths and weaknesses compared to competing methods. The methods will also be used to understand and predict the properties of cutting-edge materials in the form of metal oxide superlattices and thin films that can be fabricated and measured by experiments in the near future.The methods developed in this project will be distributed to the broader scientific community via publications, conference presentations, and open-source public software releases via an existing open-source database. The award supports the training of graduate students and a postdoctoral research associate in computational materials physics. For outreach, the research team will engage with several K-12 students participating in the New Haven Science Fair by initially helping them improve their experimental designs for their Science Fair projects, and later by helping via further mentoring and judging in the Science Fair itself.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.