Machine learning helps scientists discover new materials

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.