  Active learning has been increasingly applied to screening functional
materials from existing materials databases with desired properties. However,
the number of known materials deposited in the popular materials databases such
as ICSD and Materials Project is extremely limited and consists of just a tiny
portion of the vast chemical design space. Herein we present an active
generative inverse design method that combines active learning with a deep
variational autoencoder neural network and a generative adversarial deep neural
network model to discover new materials with a target property in the whole
chemical design space. The application of this method has allowed us to
discover new thermodynamically stable materials with high band gap (SrYF$_5$)
and semiconductors with specified band gap ranges (SrClF$_3$, CaClF$_5$,
YCl$_3$, SrC$_2$F$_3$, AlSCl, As$_2$O$_3$), all of which are verified by the
first principle DFT calculations. Our experiments show that while active
learning itself may sample chemically infeasible candidates, these samples help
to train effective screening models for filtering out materials with desired
properties from the hypothetical materials created by the generative model. The
experiments show the effectiveness of our active generative inverse design
approach.
