"High-entropy alloys represent a totally different design concept. In this case we try to mix multiple principal elements together," said study senior author Wei Chen, associate professor of materials science and engineering at the Illinois Institute of Technology.
For the study, Chen and colleagues surveyed a large space of 14 elements and the combinations that yielded high-entropy alloys. They performed high-throughput quantum mechanical calculations, which found the alloy’s stability and elastic properties, the ability to regain their size and shape from stress, of more than 7,000 high-entropy alloys. "This is to our knowledge the largest database of the elastic properties of high-entropy alloys," Chen added.
They then took this large dataset and applied a Deep Sets architecture, which is an advanced deep learning architecture that generates predictive models for the properties of new high-entropy alloys. "We developed a new machine-learning model and predicted the properties for more than 370,000 high-entropy alloy compositions," Chen said.
The last part of their study utilized what’s called association rule mining, a rule-based machine-learning method used to discover new and interesting relationships between variables, in this case how individual or combinations of elements will affect the properties of high-entropy alloys. "We derived some design rules for high-entropy alloy development. And we proposed several compositions that experimentalists can try to synthesize and make," Chen added.
High-entropy alloys are a new frontier for materials scientists. As such, there are very few experimental results. This lack of data has thus limited scientists’ capacity to design new ones. "That's why we perform the high-throughput calculations, in order to survey a very large number of high-entropy alloy spaces and understand their stability and elastic properties," Chen said.
He referred to more than 160,000 first-principle calculations in this latest work.
"The sheer number of calculations are basically not possible to perform on individual computer clusters or personal computers," Chen said. "That's why we need access to high-performance computing facilities, like those at TACC allocated by XSEDE."
"Hopefully more researchers will utilize computational tools to help them narrow down the materials that they want to synthesize," Chen said. "High-entropy alloys can be made from easily sourced elements and, hopefully, we can replace the precious metals or elements such as platinum or cobalt that have supply chain issues. These are actually strategic and sustainable materials for the future."
COMPAMED-tradefair.com; Source: University of Texas at Austin, Texas Advanced Computing Center
