Computational materials scientists at Ames Laboratory have developed an evolutionary algorithm based on a hybrid version of a computer program called Cuckoo Search, which mimics the brood parasite behavior of cuckoo birds. Image: US Department of Energy, Ames Laboratory.By enhancing an algorithm that borrows its approach from the nesting habits of cuckoo birds, computational materials scientists at the US Department of Energy's Ames Laboratory have reduced the search time for new high-tech alloys from weeks to mere seconds. They report their advance in a paper in Nature Computational Science.
The scientists are investigating high-entropy alloys, a novel class of materials that are highly sought after for their unusual and potentially beneficial properties. These alloys are lightweight in relation to their strength, fracture-resistant, highly corrosion and oxidation resistant, and stand up well in high-temperature and high-pressure environments – making them attractive materials for aerospace industry, space exploration, nuclear energy and defense applications.
While the promise of these materials is great, they present major difficulties to scientists attempting to search for and customize them for use in technologies. Because these alloys are made up of five or more different elements, they are expensive and difficult to develop and search experimentally, making an Edison-like approach a nonstarter.
With so many ingredients, and so many different ways to construct high-entropy alloys, there are nearly endless permutations of recipes for their design. Among literally billions of options, how do researchers narrow their search to a few excellent potential candidates for a specific application?
The answer in this case is an evolutionary algorithm that uses a hybrid version of a computer program developed 10 years ago, called Cuckoo Search (CS). Cuckoo birds are brood parasites, laying their eggs in the nest of a host bird so that it ends up rearing the bigger, stronger cuckoo chick as one of its own.
"This 'survival of the fittest' strategy from the behavior of birds is the idea behind Cuckoo Search," said Duane Johnson, a computational materials scientist at Ames Laboratory. Each egg represents a possible solution, competing to be the best solution in any given nest in a fixed number of possible nests. The best solution from each nest competes against other nests, until the best solution is found.
The Ames Laboratory team put a twist on the Cuckoo Search that greatly speeded up the process of locating ideal alloys, or the best 'egg', within a huge number of possibilities. The original CS takes advantage of a mathematical concept called Lévy flight, which computational theorists use to their advantage in searching extremely large data sets. But while Lévy flight works for large data sets, the Ames Lab team found that pairing it with a mathematical concept called a Monte Carlo algorithm greatly reduced the time it took to obtain optimal candidates for high-entropy alloys, providing optimal models almost on the fly.
"With the model-building bottleneck eliminated, computational design can be performed that is currently impractical," said Johnson. "As our hybrid CS is problem-agnostic, it offers application in optimization in many diverse fields."
This story is adapted from material from Ames Laboratory, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.