Researchers at Skoltech and MIPT have made a breakthrough in alloy discovery, developing a machine learning-based method that significantly speeds up the process of identifying promising metal alloy compositions for lab testing. This innovation promises to revolutionize the traditionally slow and complex process of alloy modeling, offering a more efficient way to find high-performance materials for a wide range of industrial applications.

High-entropy alloys (HEAs) have attracted significant attention in materials science due to their ability to remain stable across a variety of compositions. However, their complexity, with numerous elements and potential configurations, makes the solid solution phase more energy-efficient and favorable, yet also increases the number of competing intermetallic compounds. Understanding and identifying these compounds is crucial for effectively studying and utilizing high-entropy alloys.

In traditional alloy research, scientists rely on computational simulations and experimental trials, which are resource-intensive and often miss valuable alloy candidates due to the vast number of possible combinations. Without a systematic approach, researchers might overlook alloys with potentially useful properties. The new method developed by the Skoltech and MIPT teams uses machine learning algorithms to rapidly identify stable, promising alloys, effectively narrowing down the options for further lab testing.

Professor Alexander Shapeev, head of the Laboratory of Artificial Intelligence for Materials Design at Skoltech AI, explained the challenge, saying, “The number of potential candidates is vast because so many variables are involved: what elements make up the alloy, in which proportions, and the crystal structure.” With so many combinations to explore, the ability to quickly identify viable alloys can significantly accelerate the development of materials for industries like aerospace, mechanical engineering, electronics, and medical technology.

Alloys, which are mixtures of metals and sometimes other elements like carbon or silicon, often offer superior properties compared to pure metals. By adjusting the composition and ratios of the elements, alloys can be fine-tuned for characteristics such as strength, malleability, corrosion resistance, and electrical conductivity. However, discovering new alloys with desirable properties typically requires expensive and time-consuming lab testing, and the computational power needed for simulations can be overwhelming. This new method addresses those challenges, enabling a more comprehensive search of potential alloy candidates.

The researchers tested their machine learning-based approach on two groups of metals: five high-melting-point metals—vanadium, molybdenum, niobium, tantalum, and tungsten—and five noble metals—gold, platinum, palladium, copper, and silver. By considering different combinations of these metals, the algorithm was able to identify stable alloys by optimizing the energy and enthalpy of formation. If an alloy was found to be unstable, the algorithm would guide it toward more viable configurations.

Through this process, the researchers discovered 268 new stable alloys at zero temperature, which were not included in widely used industry databases. For example, in the niobium-molybdenum-tungsten system, the algorithm identified 12 potential alloy candidates that had not been previously documented. These alloys now offer a promising direction for further experimental validation.

While these newly discovered alloys still require more detailed testing to determine their practical applications, computational modeling has already been instrumental in discovering important industrial alloys used in products such as automotive parts and rocket fuel storage systems. The researchers are now planning to expand their algorithm to include additional alloy compositions and crystal structures, further broadening the scope of potential new materials.

This advancement in alloy discovery, powered by machine learning, could help accelerate the development of advanced materials with tailored properties, paving the way for innovations in multiple high-tech industries.

By Impact Lab