Companies in aerospace, energy, and computing constantly seek new materials to boost performance, but understanding how those materials will behave in real-world applications often requires costly and time-consuming physical testing. Traditional simulation techniques struggle to model the complex chemical arrangements found in most solid materials, particularly metal alloys. Now, MIT researchers have developed a method that accurately models the behavior of metals regardless of their chemical complexity.
At the core of the approach are machine-learning models that simulate materials atom by atom. The researchers improved these models by building training datasets that capture the diversity of atomic environments in chemically disordered materials. Using information theory, they optimized the training sets to include a wider variety of local chemical configurations, replacing redundant examples with new ones to make the data more informative.
In a paper published in Science Advances, the team demonstrated that their technique could accurately predict material properties for a diverse group of metal alloys under various conditions. The approach also showed promise for developing new materials, especially in scenarios where experimentation is expensive.
“The focus is metallic alloys, but this could be adapted to other materials like semiconductors,” says senior author Rodrigo Freitas, MIT’s TDK Career Development Professor in Materials Science and Engineering. “You could use this approach to create new sustainable steels, new materials for aerospace, and more.”
The researchers applied their technique to create machine-learning training datasets for several chemically diverse alloys. Models trained on these datasets outperformed larger models from companies like Google and Microsoft in accuracy. The team also validated their predictions against experimental data on atomic ordering in alloys.
One key test involved predicting phase diagrams—maps showing which phases of an alloy are stable at different temperatures and compositions. The models closely matched experimental data, a crucial step for connecting materials modeling to real-world processing decisions like welding, casting, or heat-treating.
The researchers are now using the method to study how changing an alloy’s composition affects mechanical properties and radiation tolerance, aiming to design materials that remain strong and damage-tolerant in harsh environments. They also plan to make the approach easier to integrate with existing tools used by materials engineers.
“Industry isn’t going to change the way they do things if what you’re creating doesn’t fit into their existing operating procedures,” Freitas notes. “The goal is to make these predictions useful where materials decisions are actually made.”
The research was supported by the U.S. Air Force Office of Scientific Research.
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