Scientists from the Los Alamos National Laboratory have devised a way of using machine learning in order to discover new materials with specific properties using an informatics-based adaptive strategy in combination with experiments. The new approach will help scientists find new materials in a manner that is more cost-effective and less time consuming than current procedures. "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," said Turab Lookman, a physicist and materials scientist at Los Alamos National Laboratory and senior author of the study. "Finding new materials has traditionally been guided by intuition and trial and error," he continued. "But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical."