From MRI machines to particle accelerators to maglev trains, superconductors have revolutionized modern technology — and they have the potential to do so much more.
“The main property of a superconducting material is that it can conduct electricity without resistance when cooled below a certain material-dependent critical temperature,” explained Elena Roxana Margine, associate professor of physics at Binghamton University.
However, this amazing quality comes at a price: the most commonly used niobium-based superconductors operate at extremely low temperatures – around 10 degrees Kelvin, which corresponds to -442 degrees Fahrenheit or -263 degrees Celsius.
For the last 50 years, scientists have searched for superconductors that can operate at higher critical temperatures – ideally room temperature, although 100 degrees Kelvin (-173 degrees Celsius or -279 degrees Fahrenheit) is acceptable for a variety of applications. Unfortunately, the high-temperature superconductors that have already been discovered are difficult to produce. For example, copper oxide-based superconductors are ceramic compounds that are brittle and difficult to fabricate into wires, while hydrogen-based superconductors can only be synthesized under extremely high pressure — so high that it resembles pressures found near Earth’s core.
Margine’s work in computational physics could potentially lead to breakthroughs in this area. Last summer, she received three National Science Foundation (NSF) grants to support this effort.
A $3.86 million grant from NSF’s Office of Advanced Cyberinfrastructure will help develop a comprehensive software ecosystem capable of modeling and predicting advanced functional properties of materials using electronic many-body structure methods. Margine is one of several Co-Principal Investigators (PIs) on the fellowship, which is led by Feliciano Giustino of the University of Texas at Austin; Binghamton’s share of the grant is $838,500.
The goal of this project is to extend and combine the complementary strengths of three software packages developed by this grant’s PIs and built-in compatibility layers for key density functional theory codes, Margine explained. This cyber infrastructure, in turn, will enable scientists to perform systematic and predictive calculations of properties that underpin the development of next-generation materials for energy, computing and quantum technologies.
Margine is the sole Principal Investigator for an ongoing $400,000 grant from NSF’s Materials Research Division that will allow her to implement new capabilities to model superconducting materials.
Another grant of $226,947 from the Division of Materials Research will support the search for superconducting materials that can operate at a higher critical temperature. The team, led by Margine and Associate Professor of Physics Alexey Kolmogorov, will study promising combinations of boron, carbon and various metals using advanced modeling methods and computational tools. Kolmogorov will use a combination of evolutionary algorithms and machine learning methods to identify synthesizable compounds, while Margine will investigate the most suitable candidate materials with potential for high-temperature superconductivity. However, that’s not as easy as opening a laptop.
Superconductivity is a complex process governed by the interaction between electrons and atomic vibrations in a material. Accurately modeling this interaction requires not only complex computer code and calculations, but also immense computational power.
“You need supercomputers to perform calculations like this,” says Margine.
For the past several years, Margine has used the Expanse cluster at the San Diego Supercomputer Center; That year she also received resources to use the Frontera supercomputer at the Texas Advanced Computing Center.
The grants also support the training of undergraduate and graduate students and postdoctoral researchers in the fields of computational materials science and high performance computing. These grants will also help develop a more diverse and inclusive STEM workforce by organizing annual schools for users of the codes, Margine said. One such training session will be held this June at the University of Texas at Austin.
Using computer modeling, researchers could predict which materials would excel as superconductors, particularly those that can operate at higher critical temperatures. Understanding how they work at the atomic level could one day lead to innovations in energy storage, medicine, electronics, transportation, and even quantum computing.
“We are trying to develop methods with improved predictive capabilities that pave the way for a rational design of new superconductors,” Margine said.