Decyphering the secrets of grain boundaries Monday, 14 August 2017

A material is only as strong as its weakest grain boundary and an American team believe they have created a universal system to predict if a certain configuration of atoms at grain boundaries will make a material stronger or more pliable.

The interdisciplinary team from Brigham Young University (BYU) came up with a computer algorithm that allows it to learn the elusive “why” behind the boundaries’ qualities.

Their method provides a technique to produce a 'dictionary' of the atomic building blocks found in metals, alloys, semiconductors and other materials. Their machine learning approach analyses large data sets of grain boundaries to provide insight into physical structures that are likely associated with specific mechanisms, processes and properties that would otherwise be difficult to identify.

“We’re using machine learning, which means algorithms can see trends in lots and lots of data that a human can’t see,” said BYU mechanical engineering professor Eric Homer.

“With Big Data models you lose some precision, but we’ve found it still provides strong enough information to connect the dots between a boundary and a property.”

When it comes to metals, the process can evaluate properties like strength, weight and lifespan of materials, leading to the eventual optimisation of the best materials. Although the group is not actually creating materials yet, they feel they can now decipher the 'why' and the 'how' of the makeup.

They say their paper is the first to attempt to crack the code of the atomic structures that heavily influence grain boundary properties with the computer algorithms of machine learning.

“Our nation spends $500 billion a year on corrosion,” Homer said. “If you can reduce the cost of treating corrosion even a few percent by developing more resistant metals, you can save billions every year. That’s not a small amount of money.”

[The BYU research team of (from left) Eric Homer, Conrad Rosenbrock and Gus Hart. Photo: BYU]