ABSTRACT

Non-technical summary

This EAGER award supports a joint computational and theoretical effort to guide the search for practical superconducting materials. Superconductors carry electrical current without any resistance when cooled down below a certain material-dependent critical temperature. This remarkable property has already found numerous applications, from maglev trains to the Large Hadron Collider, but present-day superconductors are difficult to manufacture or require ultra-low temperatures to function. New superconducting materials that can be mass-produced and operate at easily maintained temperatures have the potential to revolutionize energy, transportation, communication, and other emerging technologies.

Design of new superconductors is notoriously difficult, because their properties are sensitive to the chemical composition and crystal structure. In this project, the team will focus on exploring promising combinations of light abundant elements including boron, carbon, and various metals. The PIs will employ advanced modeling methods and computational tools developed in their groups to identify and analyze suitable candidate materials. The search for stable compounds will be performed with a combination of an evolutionary algorithm and machine-learning interatomic potentials. Viable compounds will be examined with a computational method based on Wannier functions, a state-of-the-art approach for predicting superconducting properties.

The PIs will contribute to the development of the next generation of scientists by organizing outreach activities for elementary-school students and involving students from underrepresented groups into STEM research. All new computational features added to the team's open-source packages will be made publicly available.


Technical summary

This EAGER award supports research aiming to identify quasi-two-dimensional doped-covalent-bond light-weight materials with potential for high-temperature conventional superconductivity at ambient pressure. With the long-term goal of screening a large compositional space centered on second-row elements forming covalent frameworks and light metals improving the compounds' stability, the team will first perform a systematic ab-initio exploration of the Li-M-B-C compositions. For predicting synthesizable materials, the team will use the previously developed Module for Ab-Initio Structure Evolution (MAISE) platform to accelerate the identification of stable compounds with a combination of evolutionary structure optimization and machine-learning interatomic potentials. For modeling superconducting properties, the team will rely on the Electron-Phonon Wannier (EPW) code based on Wannier functions, which enables resolving superconducting anisotropy within the Eliashberg theory. An integral part of the research is the development of physics-based rules for the rational design of superconductors, which will be achieved by finding descriptors correlating easy-to-calculate structural, electronic, and vibrational properties with superconducting features.

The proposed work will offer an opportunity for undergraduate and graduate students to acquire knowledge in advanced electronic-structure methods, computational materials science, and high-performance computing. The PIs will continue to organize workshops and webinars to teach the underlying theory and optimal usage of EPW and MAISE to the broader materials-research community.

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.

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