Particle physics research

Research could lead to the development of new superconductors

From MRI machines to particle accelerators and 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 the University of Binghamton.

This amazing quality comes at a cost, however: the most commonly used niobium-based superconductors operate at extremely low temperatures – around 10 degrees Kelvin, which is equivalent to -442 degrees Fahrenheit or -263 degrees Celsius.

Over the past 50 years, scientists have searched for superconductors capable of operating at higher critical temperatures – ideally at room temperature, although 100 degrees Kelvin (-173 degrees Celsius or -279 degrees Fahrenheit) is acceptable for a wide range of applications. Unfortunately, the high-temperature superconductors already discovered are difficult to fabricate. Copper oxide superconductors are ceramic compounds, for example, which are brittle and difficult to form into wires, while hydrogen superconductors can only be synthesized under extremely high pressure – so high, in fact, that it is similar to the pressures found near Earth’s Core.

Margine’s work in computational physics could potentially lead to breakthroughs in this field. Last summer, she received three grants from the National Science Foundation (NSF) to support this effort.

A $3.86 million grant from NSF’s Office of Advanced Cyberinfrastructure will help develop a comprehensive software ecosystem to model and predict advanced functional properties of materials using many-body electronic structure methods. Margine is one of several co-principal investigators (PIs) on the grant, which is led by Feliciano Giustino of the University of Texas at Austin; Binghamton’s portion 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 the IPs of this grant and built-in compatibility layers for major density functional theory codes, Margine explained. This cyberinfrastructure, in turn, will allow scientists to perform systematic and predictive calculations of the 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 Division of Materials Research that will allow her to implement new capabilities in modeling superconducting materials.

Another $226,947 grant from the Materials Research Division will help research superconducting materials that can operate at a higher critical temperature. The team, led by Margine and Associate Professor of Physics Alexey Kolmogorov, will explore 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. It’s not as simple as opening a laptop, however.

Superconductivity is a complex process determined by the interaction between electrons and atomic vibrations in a material. Accurate modeling of this interaction not only requires complex computer codes and calculations, but also immense processing power.

“To run calculations like this, you need supercomputers,” Margine said.

For the past several years, Margine has used the Expanse cluster at the San Diego Supercomputer Center; this 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, as well as postdoctoral researchers in computational materials science and high performance computing. These grants will also help develop a more diverse and inclusive STEM workforce by hosting annual schools for code users, Margine said. One of these training sessions will take place in June at the University of Texas at Austin.

Using computer modeling, researchers might be able to predict which materials would excel as superconductors, especially 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.

“What we’re trying to do is develop methods with improved prediction capabilities that will pave the way for the rational design of new superconductors,” Margine said.