Collaborative Research: DMREF: Discovery of unconventional superconductors by design

Harvard University, Cambridge MA

Investigators

Abstract

Non-technical abstract: Superconductivity is a quantum phenomenon where electricity flows with zero resistance below a critical temperature. High temperature superconductivity has the promise to revolutionize energy generation, storage, and distribution. Despite the discoveries of unconventional, high-temperature superconductivity in copper and iron-based materials, known superconductors cannot be widely exploited as they become superconducting at very low temperatures. Discovering new superconductors and increasing their transition temperatures has been limited by the lack of consensus about the necessary ingredients that give rise to superconductivity in these families of materials. This DMREF project seeks to exploit the characteristics (descriptors) of the known copper- and iron-based superconductors as design criteria in the search for new ambient-pressure superconducting materials. Promising materials will be synthesized in a tight feedback loop between theory and experiment. This project will also provide educational opportunities for students in high school and at the beginning of their undergraduate studies. Technical Abstract: This DMREF project will accelerate the discovery of new superconducting materials through a multi-disciplinary team that unites experience in materials synthesis, local probes, and computation. To design and simulate the new superconductors this research will use computational tools including density functional theory (DFT) and dynamical mean field theory (DMFT) to predict both the crystal and electronic structures of candidate correlated electron materials. The most promising predicted materials will then be synthesized by combining two traditionally disparate forms of materials synthesis -- advanced thin film deposition by reactive oxide molecular beam epitaxy and low-temperature soft chemistry -- to make metastable materials that could not previously be realized. The materials will then be interrogated by bulk and local probes to map resistivity, structure, orbital polarization/hybridization, phonons, and spin interactions. Our approach closes the loop between the prediction of materials with our high temperature superconductivity descriptors and direct measurement of these properties. The resulting large datasets will be Artificial Intelligence-ready and can be correlated to build structure-property relationships. 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.