  Materials with high-dielectric constant easily polarize under external
electric fields, allowing them to perform essential functions in many modern
electronic devices. Their practical utility is determined by two conflicting
properties: high dielectric constants tend to occur in materials with narrow
band gaps, limiting the operating voltage before dielectric breakdown. We
present a high-throughput workflow that combines element substitution, ML
pre-screening, ab initio simulation and human expert intuition to efficiently
explore the vast space of unknown materials for potential dielectrics, leading
to the synthesis and characterization of two novel dielectric materials,
CsTaTeO6 and Bi2Zr2O7. Our key idea is to deploy ML in a multi-objective
optimization setting with concave Pareto front. While usually considered more
challenging than single-objective optimization, we argue and show preliminary
evidence that the $1/x$-correlation between band gap and permittivity in fact
makes the task more amenable to ML methods by allowing separate models for band
gap and permittivity to each operate in regions of good training support while
still predicting materials of exceptional merit. To our knowledge, this is the
first instance of successful ML-guided multi-objective materials optimization
achieving experimental synthesis and characterization. CsTaTeO6 is a structure
generated via element substitution not present in our reference data sources,
thus exemplifying successful de-novo materials design. Meanwhile, we report the
first high-purity synthesis and dielectric characterization of Bi2Zr2O7 with a
band gap of 2.27 eV and a permittivity of 20.5, meeting all target metrics of
our multi-objective search.
