Structure Complements: A New Materials Taxonomy

We present a new paradigm of materials taxonomy, dubbed "structure complements," by generalizing anti-structures (or inverse structures). Materials properties depend strongly on crystal geometry but also on the distribution of charge. Thus, our algorithm for classifying materials by geometry and cation/anion decoration proves not only useful as a novel categorization scheme but also as a framework for targeted materials discovery. As a use case, we show a workflow which combines structure complement analysis, a transparent machine learning model, and high throughput density functional theory calculations to discover novel ferroelectric materials. The workflow is designed to be integrated into an autonomous, closed-loop materials discovery platform which integrates a unified materials database, machine learning, simulation, and high-throughput synthesis and characterization.