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Faster forecasting: How will materials respond to extreme temperatures?

Image: Courtesy of Miladin Radovic

Will it be possible to design materials that are unfazed by extreme temperatures in the near future?

In a study published in the journal Nature Computational Materials, researchers at Texas A&M University have described a computational tool to evaluate a material’s suitability for high-temperature applications, such as gas turbines for jet engines and electrical power generators. The computational framework, which incorporates artificial intelligence and basic physics, can forecast how materials will behave under harsh conditions in a fraction of the time compared to other algorithms.

“We have used an innovative and interdisciplinary approach for screening materials that is a million times faster than traditional techniques,” said Raymundo Arróyave, professor in the Department of Materials Science & Engineering at Texas A&M University and corresponding author on the study. “Currently, these types of calculations, even for a small temperature above absolute zero, are an enormous challenge because they are computationally expensive.”

Since the late 1800s, gas turbines have been the workhorse of power generation. This drum-shaped machine lined with a series of bent or curved blades converts chemical energy from burning fuel into mechanical energy when the turbine’s blades rotate. This motion is then exploited either to propel an aircraft or generate electricity.

Gas turbines operate in high-temperature, corrosive conditions, making them prone to damage and progressive deterioration. And so, designing materials that can withstand extreme temperatures has been an ongoing pursuit. 

Among an array of high-temperature tolerant materials, ceramics known as MAX phases, are known to have properties that bridge the gap between conventional ceramics and metals. In other words, they are less brittle than ceramics and have higher temperature tolerance than many metals.

“These materials are ideal candidates for structural components for gas turbines and heat-resistant coatings,” said Miladin Radovic, professor in the materials science and engineering department and a senior author on the study. “However, only a few out of hundreds of possible MAX phases have been experimentally verified to be high-temperature corrosion and oxidation-resistant.”

The researchers noted that given the vast number of elements that can be used to make MAX phases and an even greater number of ways of combining them, the task of experimentally verifying how each composite will behave at high temperatures becomes impractical. On the other hand, computational techniques, such as purely machine-learning algorithms, have not been as robust at predicting the material’s behavior at nonzero temperatures.

Instead of relying solely on just one method, Arróyave and his team used a three-pronged approach that included a combination of density functional theory, machine learning and computational thermodynamics.

The researchers first calculated some fundamental properties of MAX phases at zero kelvins with density functional theory. Next, those calculations were used as inputs to a machine-learning model. In this way, the researchers replaced otherwise computationally expensive calculations from density functional theory with machine-learning models. Then, they used computational thermodynamics to determine the most stable compounds for a given temperature and a certain MAX phase composition.

“Let’s consider a MAX phase made of titanium, aluminum and carbon. At higher temperatures, we could have, for example, carbon dioxide, carbon monoxide, and other combinations of carbon and oxygen that might compete to exist,” said Arróyave. “Using our framework, one can now determine which phases or combinations we can expect at that temperature, how much of it and whether that can be detrimental. Simply put, we can now quickly tell whether the material will decompose at a given temperature.”

This research is funded by the National Science Foundation.