Adaptive machine learning for efficient materials design

Applying machine learning (ML) methods to accelerate the search for new materials with improved properties has gained increasing attention in recent years. Using nonadaptive ML approaches that do not have an iterative feedback loop can perform poorly in extrapolations at previously unexplored search space, especially when trained on small data sets. We performed numerical simulations on two data sets that exhibit distinct composition-property relationships and explored the relative efficacies of adaptive ML strategies in identifying the optimal material composition with the highest. Adaptive ML methods show promise for extrapolation and find compositions with properties better than those in the training data, but the rate of discovery is dictated by the nuances of the composition-property landscape. The outcome of this work has key implications in developing strategies that employ ML methods for navigating a vast search space of combinatorial possibilities.