LOS ALAMOS, N.M., May 9 (UPI) -- Traditionally, materials scientists have used a combination of trial-and-error and intuition to discover and perfect new materials with advantageous properties. Increasing chemical complexities make this strategy prohibitively time-consuming.
To speed up the process, researchers at Los Alamos National Laboratory attempted to marry machine learning with targeted experiments. The team's "informatics-based adaptive design strategy" successfully accelerated the materials discovery process.
"What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," Turab Lookman, a physicist and materials scientist at Los Alamos, said in a news release.
A machine-learning algorithm powered by Los Alamos' high-performance supercomputers effectively digitized the trial-and-error process. Lab experiments help work out uncertainties produced by the algorithm.
"The goal is to cut in half the time and cost of bringing materials to market," said Lookman. "What we have demonstrated is a data-driven framework built on the foundations of machine learning and design that can lead to discovering new materials with targeted properties much faster than before."
Lookman and his colleagues used to the strategy to guide their search for a shape-memory alloy with low thermal dissipation, but the researchers say machine learning could guide discoveries with other material classes and for other material qualities.
Researchers published their work in the journal Nature Communications last month.