  Beyond the conventional trial-and-error method, machine learning offers a
great opportunity to accelerate the discovery of functional materials, but
still often suffers from difficulties such as limited materials data and
unbalanced distribution of target property. Here, we propose the ab initio
Bayesian active learning method that combines active learning and
high-throughput ab initio calculations to accelerate prediction of desired
functional materials with the ultrahigh efficiency and accuracy. We apply it as
an instance to a large family (3,119) of two-dimensional hexagonal binary
compounds with unbalanced materials property, and accurately screen out the
materials with maximal electric polarization and proper photovoltaic band gaps,
respectively, whereas the computational costs are significantly reduced by only
calculating a few tenths of possible candidates in comparison to the random
search. This approach shows enormous advantages for the cases with unbalanced
distributions of target property. It can be readily applied to seek for a broad
range of advanced materials.
