Research Highlights: Eva Zurek

UB chemistry researcher Eva Zurek is a key partner on a new $7.5 million effort to discover inexpensive materials hard enough to join two pieces of steel together through a process called friction stir welding.

The team will also develop a suite of artificial intelligence (AI)-based tools for the on-demand design of similar materials with properties tailored to a range of applications. The collaboration is led by Duke University’s Center for Autonomous Materials Design and is funded by a five-year grant through the Department of Defense’s Multidisciplinary University Research Initiative (MURI). Zurek, professor of chemistry in the College of Arts and Sciences, will receive nearly $1.2 million. The project also includes researchers from Pennsylvania State University, North Carolina State University and Missouri University of Science and Technology.

High-entropy materials with many components

The focus of the research is on “high-entropy” materials that combine several elements to create a complex structure that derives enhanced stability from a chaotic mixture of atoms. After demonstrating this approach with carbides in 2018, the researchers will now look to add borides into the irregular self-organized structures to produce some of the hardest materials ever made. The goal is to mix at least two high-entropy materials together so that they interlock, similar to a grid formed from a variety of Tetris pieces. This configuration contributes to hardness.

“We’ve already developed the computational machinery needed to tell us when this phenomenon will produce these stable, super-hard materials,” said Stefano Curtarolo, professor of mechanical engineering and materials science at Duke and leader of the new MURI award. “Our goal now is to develop the necessary ‘cooking’ procedures, as well as AI-materials tools that can automate the discovery of new recipes to fit different needs.”

One material the team will explore is a combination of carbon, boron, nitrogen and five other inexpensive metallic elements, all stabilized by entropy. That complexity is part of what makes the research so exciting, says Zurek, a theoretical and computational chemist. Her team at UB will use machine learning tools to develop interatomic potentials that can be employed to perform large-scale simulations of the interlocking boundaries between the two high-entropy materials, and facilitate the discovery of new, hard, synthesizable materials using AI.

“I’m really excited about this,” Zurek says. “I think this is a way to go in order for theoreticians to simulate the dynamic behavior of chemical systems of thousands of atoms for hundreds of picoseconds, a relatively long period of time in this field, so that we can present more interesting targets for experimentalists. In our newly funded project, we want to find new super-hard materials that are composed of up to 10 different chemical systems or elements.”