  The discovery of high-dielectric materials is crucial to increasing the
efficiency of electronic devices and batteries. Here, we report three
previously unexplored materials with very high dielectric constants (69 $<$
$\epsilon$ $<$ 101) and large band gaps (2.9$<$ $E_{\text{g}}$(eV) $<$ 5.5)
obtained by screening materials databases using statistical optimization
algorithms aided by artificial neural networks (ANN). Two of these new
dielectrics are mixed-anion compounds (Eu$_5$SiCl$_6$O$_4$ and HoClO), and are
shown to be thermodynamically stable against common semiconductors via
phase-diagram analysis. We also uncovered four other materials with relatively
large dielectric constants (20$<$$\epsilon$$<$40) and band gaps
(2.3$<$$E_{\text{g}}$(eV)$<$2.7). While the ANN training data is obtained from
Materials Project, the search-space consists of materials from Open Quantum
Materials Database (OQMD) - demonstrating a successful implementation of
cross-database materials design. Overall, we report dielectric properties of 17
materials calculated using ab-initio calculations, that were selected in our
design workflow. The dielectric materials with high dielectric properties
predicted in this work open up further experimental research opportunities.
