New materials could boost fighter jet efficiency and performance

In aerospace applications, high-temperature shape memory alloys (HTSMAs) — materials capable of remembering and returning to their original shapes after heating — are often constrained by high costs since they rely on expensive elements to function at elevated temperatures. 

Fighter jets like the F/A-18 need to fold their wings to fit on crowded aircraft carriers. The system that folds the wings relies on heavy mechanical parts. But with new lighter, smarter alloys, those movements could be done with less weight and more efficiency. That means more jets can be ready to fly, faster and with less energy wasted.

The researchers have published their findings in the journal Acta Materialia. The research showcases how artificial intelligence (AI) and high-throughput experimentation can be combined to accelerate materials discovery and reduce development costs. 

This pioneering study sets the stage for a new era in functional alloy design — faster, cheaper, and smarter. That means everything that can use this process could soon be made with materials that are not only more efficient but also more affordable for those seeking to save on costs. 

The team is being led by Department Head and Chevron Professor Dr. Ibrahim Karaman and Chevron Professor II Dr. Raymundo Arroyave, who have developed a data-driven approach to material discovery. 

“This work shows that we can design better high-temperature alloys not through expensive trial-and-error but through smart, targeted exploration driven by data and physics,” said Karman. 

“This project is exciting as it shows the power of the advanced alloy development frameworks we have been developing in the past years.” Arroyave added.

Background

Designing new metals usually takes a lot of time and money. Scientists must test thousands of metal mixtures to find the right one — and even tiny changes, like adding just 0.1% more of one element, can totally change how the material behaves. With so many options, finding the correct combination for the alloy would be like guessing the right lottery numbers.

To speed things up, researchers at Texas A&M University are using powerful computers and artificial intelligence. These tools help them predict how different metal mixtures would interact, so they don’t have to test every single option in their labs. Instead, they utilize AI to help them concentrate their efforts on the more promising ones.

“With advanced computational tools, we’re not just speeding up alloy discovery — we’re reshaping how discovery happens,” said Sina Hossein Zadeh, a Ph.D. student in the Department of Materials Science and Engineering.

Research Innovation

The standout feature of this project is its integration of machine learning and experimental work through a framework known as Batch Bayesian Optimization (BBO). BBO allows scientists to iteratively refine their alloy predictions based on past experimental results, minimizing waste and maximizing discovery efficiency.

“This framework not only speeds up discovery,” Karaman says, “but also opens the door to tailoring alloys for specific functions, such as reducing energy loss or improving actuation performance in many applications.” 

The goal is to make materials that move or change shape in response to something like heat or electricity — kind of like muscles for machines. These special materials are called actuators, and they’re often used in aerospace, robotics, and even medical devices.

Challenges and the Future

Currently, the model explores alloying with copper and hafnium to enhance shape memory behavior and raise transformation temperatures. However, further research is needed to incorporate more elements and predict additional performance metrics, such as transformation strain — how much the alloy can actually move or change shape during operation.

According to Broucek, “The next frontier is to design alloys that not only transform at the right temperatures but also deliver meaningful strain in service. That’s what will make them viable for aerospace and energy applications.”

By Leon Contreras, Texas A&M University College of Engineering

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