  Machine learning models of materials$^{1-5}$ accelerate discovery compared to
ab initio methods: deep learning models now reproduce density functional theory
(DFT)-calculated results at one hundred thousandths of the cost of DFT$^{6}$.
To provide guidance in experimental materials synthesis, these need to be
coupled with an accurate yet effective search algorithm and training data
consistent with experimental observations. Here we report an evolutionary
algorithm powered search which uses machine-learned surrogate models trained on
high-throughput hybrid functional DFT data benchmarked against experimental
bandgaps: Deep Adaptive Regressive Weighted Intelligent Network (DARWIN). The
strategy enables efficient search over the materials space of ~10$^8$ ternaries
and 10$^{11}$ quaternaries$^{7}$ for candidates with target properties. It
provides interpretable design rules, such as our finding that the difference in
the electronegativity between the halide and B-site cation being a strong
predictor of ternary structural stability. As an example, when we seek UV
emission, DARWIN predicts K$_2$CuX$_3$ (X = Cl, Br) as a promising materials
family, based on its electronegativity difference. We synthesized and found
these materials to be stable, direct bandgap UV emitters. The approach also
allows knowledge distillation for use by humans.
