From automobiles to artificial satellites, from building materials to electronic products, high-grade metal alloys are essential in key parts of modern life. However, due to researchers’ vague understanding of the boundaries between the tiny crystal grains that make up most metals, the development of new alloys with the best strength, hardness, corrosion resistance, conductivity and other properties for specific purposes is limited.
When two metals are mixed together, the atoms of the secondary metal may gather along these grain boundaries, or they may be scattered through the atomic lattice within the grain. The overall performance of materials depends to a large extent on the behavior of these atoms, but so far, there is no systematic method to predict their effects.
MIT researchers have now found a method that combines computer simulation and machine learning processes to make various detailed predictions of these properties, which can guide the development of new alloys in various applications. The research results are described in the journal Nature Communications. The paper was written by graduate student Malik Wagih, postdoctoral fellow Peter Larsen, and professor of materials science and engineering Christopher Schuh.
Schuh explained that understanding the atomic-level behavior of polycrystalline metals for most of the metals we use is a daunting challenge. Although the atoms in a single crystal are arranged in an orderly manner, the relationship between adjacent atoms is simple and predictable. This is not the case with multiple tiny crystals in most metal objects. He said: “In what we call grain boundaries, crystals are crushed together. In traditional structural materials, there are thousands of such grain boundaries.”
These boundaries help determine the characteristics of the material. He said: “You can think of them as glue that holds the crystals together.” “But they are disordered and the atoms are messed up. They don’t match the crystals they are added to.” He said, this This means that they provide billions of possible atomic arrangements, and there are only a few arrangements in the crystal. Creating new alloys involves “trying to design these areas inside the metal, which is actually billions of times more complicated than designing in crystals.”
Schuh made an analogy to the people nearby. “It’s a bit like in the suburbs. You might have 12 neighbors around you. In most metals, you look around and you see 12 people, and they are at the same distance from you. This is completely homogeneous. In a grain On the border, you still have 12 neighbors, but their distances are different, and they are all houses of different sizes in different directions.”
He said that traditionally, those designing new alloys simply skipped this question, or just looked at the average characteristics of the grain boundaries with the same eyes, even if they knew they were not.
Instead, the team decided to rigorously solve the problem by examining the actual distribution of the configuration and interaction of a large number of typical cases, and then using machine learning algorithms to infer from these specific cases and provide predictions for the entire range.
In some cases, the aggregation of atoms along the grain boundary is an ideal property that can enhance the hardness and corrosion resistance of the metal, but sometimes it can also lead to embrittlement. Depending on the intended use of the alloy, engineers will try to optimize the combination of properties. In this study, the research team studied more than 200 different combinations of base metals and alloy metals based on the combinations described at the basic level in the literature. Then, the researchers systematically simulated some of these compounds to study their grain boundary configuration. These are used to generate predictions through machine learning, which are then verified through more focused simulations. The machine learning predictions match the detailed measurements very well.
As a result, Waji said, the researchers were able to prove that many alloy combinations that were actually considered infeasible were feasible. He said that the new database compiled by this research is already available in the public domain, and it can help anyone who is designing a new alloy.
The team is advancing the analysis. Schuh said: “In our ideal world, what we have to do is to add every metal in the periodic table to it, and then add all the other elements in the periodic table to the periodic table.” “So , You use the periodic table of the elements, cross it with yourself, and then check all possible combinations.” He said that for most of these combinations, basic data is not yet available, but with more and more simulations and collections The data can be integrated into the new system.
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