CMMT: Slave-boson approach for electronically correlated metal oxides

Yale University, New Haven CT

Investigators

Abstract

NONTECHNICAL SUMMARY This 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 SUMMARY This 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.