At temperatures around 2,500 °F (1,371 °C), steel loses its structural integrity as it melts. Aluminum, when exposed to moisture and oxygen, undergoes corrosion. These conventional alloys, although adequate for regular conditions, fail when subjected to extreme environments like intense heat, severe cold, high pressure, and other harsh conditions.
As the United States continues to expand its operations in challenging environments such as the Arctic and even space, the demand for alloys that can retain their strength under these conditions is increasingly urgent.
MPEAs: A Promising Solution?
Multi-principal element alloys (MPEAs), composed of several elements in nearly equal proportions, are emerging as a promising solution for such extreme environments. MPEAs offer high strength, hardness, and toughness across a wide range of temperatures. They also often demonstrate excellent corrosion resistance, thermal stability, and unique functional properties that could be beneficial for electronic or magnetic applications.
Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, USA are advancing MPEA design by developing complex microstructures that extract extensive composition data from just a few samples. These samples contain numerous localized regions rich in valuable data.
New Research Method
The research team has devised a method that automatically correlates the phases of alloys—distinct structures that form when an alloy is heated or cooled—with their mechanical properties. In their work published in the journal Data in Brief, they detailed a new design capability that allowed them to synthesize 17 unique MPEA compositions.
“Designing MPEAs is challenging because even a minor change in composition can significantly impact material formation and properties,” says Morgan Trexler, program manager for Science of Extreme and Multifunctional Materials at APL. “This new capability enables researchers to quickly generate and analyze vast amounts of local data within bulk samples, which will intelligently guide the design of new materials and expedite material discovery.”
To determine which MPEA compositions to manufacture, APL collaborated with Paulette Clancy and Maitreyee Sharma Priyadarshini from the Johns Hopkins Whiting School of Engineering. With limited data, Clancy and Sharma Priyadarshini utilized their physics-informed Bayesian optimization algorithm, PAL 2.0, to swiftly sift through potential alloy compositions, recommending MPEAs that would maximize hardness while providing a broad spectrum of data for future alloy research.
Unlike many deep-learning approaches that require large datasets, the team aimed to see if a minimal amount of data could still yield accurate results. Remarkably, their algorithm needed only about a dozen data points to make effective recommendations.
Lisa Pogue, a researcher at APL, described how the team used arc melting to create the alloys, a process where an electric current melts the metal. Arc melting is efficient, requiring minimal material and allowing for the rapid sampling of various compositions.
“When the alloy melts, it forms dozens of distinct materials with varying chemical compositions within a single sample,” notes Eddie Gienger, a materials scientist at APL and the lead author of the study.
Data from Multiple Sources
The new process integrates data from multiple sources—scanning electron microscopy (SEM) and energy-dispersive x-ray spectroscopy (EDS) for phase composition analysis, along with nanoindentation, which uses a tiny triangular tip to measure hardness at numerous points across the sample.
“With nanoindentation, we can automatically collect measurements from hundreds of locations on the sample and then map those indents to phase compositions identified using EDS and SEM,” Pogue adds. “This helps us understand how each unique microstructure and composition behaves.”
The collected data, which included information on 17 unique MPEA compositions and more than 7,000 data points, was compiled into a database for future alloy development.
“This process helps map out the potential for MPEAs,” Gienger says. “If we need to design a material with specific mechanical properties like hardness, this pipeline can provide the necessary information to engineer those properties.”
This technique is one of several that APL is developing to accelerate materials discovery and understanding.
“The more tools we have for high-throughput characterization,” Trexler says, “the more opportunities we have to apply these methods in areas where new, stronger materials are needed.”
Editor’s note: This article first appeared in the November 2024 print issue of Materials Performance (MP) Magazine. Reprinted with permission.