Researchers Develop a Quantum Mechanical Approach to Determining Metal Ductility

metal ductility experiment

Insider Brief

  • Researchers developed a new way to predict how well a material can withstand physical strain without cracking or breaking, or metal ductility.
  • Currently, there are currently no robust ways to predict metal ductility.
  • The quantum-mechanics-based approach fills a need for an inexpensive, efficient, high-throughput way to predict ductility.
  • Image: U.S. Department of Energy Ames National Laboratory

PRESS RELEASE — A team of scientists from Ames National Laboratory and Texas A&M University developed a new way to predict metal ductility. This quantum-mechanics-based approach fills a need for an inexpensive, efficient, high-throughput way to predict ductility. The team demonstrated its effectiveness on refractory multi-principal-element alloys. These are materials of interest for use in high-temperature conditions, however, they frequently lack necessary ductility for potential applications in aerospace, fusion reactors, and land-based turbines.

Ductility describes how well a material can withstand physical strain without cracking or breaking. According to Prashant Singh, a scientist at Ames Lab and leader of the theoretical design efforts, there are currently no robust ways to predict metal ductility. Additionally, trial-and-error experimentation is expensive and time-consuming, especially in extreme conditions.

A typical way to model atoms is with rigid spheres that are symmetrical. However, Singh explained that in real materials, the atoms are different sizes and have shapes. When mixing elements with different sized atoms, the atoms continually adjust to fit within the fixed space. This behavior creates local atomic distortion.

The new analysis incorporates local atomic distortion in determining whether a material is brittle or ductile. It also expands on the capabilities of current approaches. “They [current approaches] are not very efficient at distinguishing between ductile and brittle systems for small compositional changes. But the new approach can capture such non-trivial details, because now we have added a quantum mechanical feature in the approach that was missing,” Singh said.

Another advantage to this new high-throughput testing method is its efficiency. Singh explained that it can test thousands of materials rapidly. The speed and capacity make it possible to predict which material combinations are worth taking to the experimental level. This minimizes the time and resources needed to discover these materials through experimental methods.

To determine how well their ductility test worked, Gaoyuan Ouyang, an Ames Lab Scientist, led the team’s experimental efforts. They performed validation tests on a set of predicted refractory multi-principal-element alloys (RMPEAs). RMPEAs are materials that have potential for use in high temperature environments, such as aerospace propulsion systems, nuclear reactors, turbines, and other energy applications.

Through their validation testing, the team found that, “The predicted ductile metals underwent significant deformation under high stress, while the brittle metal cracked under similar loads, confirming the robustness of new quantum mechanical method,” Ouyang said.

This research is further discussed in the paper, “A ductility metric for refractory-based multi-principal-element alloys,” written by Prashant Singh, Brent Vela, Gaoyuan Ouyang, Nicolas Argibay, Jun Cui, Raymundo Arroyave, and Duane D. Johnson, and published in Acta Materialia.

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Matt Swayne

With a several-decades long background in journalism and communications, Matt Swayne has worked as a science communicator for an R1 university for more than 12 years, specializing in translating high tech and deep tech for the general audience. He has served as a writer, editor and analyst at The Quantum Insider since its inception. In addition to his service as a science communicator, Matt also develops courses to improve the media and communications skills of scientists and has taught courses. [email protected]

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The Future of Materials Discovery: Reducing R&D Costs significantly with GenMat’s AI and Machine Learning Tools

When: July 13, 2023 at 11:30am

What: GenMat Webinar

Jake Vikoren

Jake Vikoren

Company Speaker

Deep Prasad

Deep Prasad

Company Speaker

Araceli Venegas

Araceli Venegas

Company Speaker

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