Theoretical Prediction of Hydrogen Rich High-Temperature Superconductors

NONTECHNICAL SUMMARY

This award supports theoretical research and education aimed to advance computational design of superconductors. Superconducting materials are technologically important because electrical current can flow through them without energy loss, and because their interiors expel magnetic fields. These properties enable superconductors to be employed as cables in SmartGrid projects, in levitating trains, and as electromagnets used in Magnetic Resonance Imaging (MRI) machines and wind-turbines.

Unfortunately, all the superconductors that are technologically useful must be cooled to very low temperatures, below the frigid temperature where liquid nitrogen boils. Finding materials that behave as superconductors at room temperature would revolutionize electrical infrastructure, health care, and impact our lives in unimaginable ways. Hydrogen-rich solids are the focus of this project because research suggests that they could behave as superconductors at high temperatures.

Just as diamonds can be synthesized at high pressures deep within the Earth, researchers can vary pressure to create new materials with unusual properties. Several superconductors have recently been synthesized in this way, exhibiting superconductivity onset approaching room temperature. Many of these have been computationally predicted and could be materials by design success stories.

The PI will carry out quantum mechanical calculations to predict promising new superconducting targets for synthesis and will collaborate with leading experimental groups in high-pressure research that will attempt to create these materials. A focus of the work is finding materials that are superconductors at temperatures as high as room temperature at lower pressures than current highest temperature superconducting materials.

To advance this goal the PI will further develop software that can predict the structure of a solid without any experimental information. Machine learning methods will be interfaced with the crystal structure prediction software to accelerate the calculations. The resulting programs will be made freely available to the materials science, physics, and chemistry communities, facilitating the advance of rational materials design as well as current and future discoveries in science and engineering.

Graduate and undergraduate students will be trained in computational materials discovery as part of this project. Aiming to broaden their participation, undergraduate students from underrepresented groups will be trained in computational modelling and materials prediction via personnel exchange, paving the way for future career opportunities in STEM fields.

TECHNICAL SUMMARY

This award supports theoretical and computational research and education that will lead towards the rational design of novel hydrogen-rich superconductors. The PI will computationally predict the crystal structures of hydrides with unique stoichiometries and structures that can be synthesized under pressure and study their electronic structure and properties via first-principles calculations.

The focus will be on computationally mapping out the phase diagrams of ternary hydrides as a function of pressure. These systems are currently under intense investigation, since research suggests they may behave as superconductors at higher temperatures or lower pressures than the binary hydrides that have been recently studied intensively. The computational predictions will be confirmed by leading experimental groups in high-pressure research.

The XtalOpt evolutionary algorithm that can be used to predict the structure of an extended system given only its stoichiometry, will be further developed. Machine learning methods that will accelerate the progress of the crystal structure searches and focus them on materials that are likely to have the highest superconducting critical temperatures, will be interfaced with XtalOpt.

The crystallography suite within the highly popular chemical builder, editor, and visualizer Avogadro, will be further advanced. XtalOpt and Avogadro are open-source software, and their development contributes to the creation of cyberinfrastructure, facilitating current and future discoveries in science and engineering.

Graduate and undergraduate students will be trained in rational computational materials design and programming, thereby preparing them for future careers where synergy between theory, computation, and experiment leads to innovation. Collaboration with primarily undergraduate, minority-serving institutions that involves student and faculty exchange will expose students from underrepresented groups to research and future career opportunities in STEM fields and train them in first-principles modelling techniques.

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.