| Abstract Scope |
We present a new paradigm of materials taxonomy, dubbed “decoratypes” (a portmanteau of decoration and prototype), to describe the set of all possible decorations of an isopointal structure type. This taxonomy generalizes anti-structures (or inverse structures). Materials properties depend strongly on crystal geometry and the distribution of charge. Thus, our algorithm for classifying materials by geometry and ion decoration proves useful both as a novel categorization scheme and as a framework for targeted materials discovery. As a use case, we showcase a workflow which combines structure complement analysis, a transparent machine learning model, and high throughput DFT calculations to discover novel ferroelectric materials. We then examine the microscopic origins of ferroelectricity in these new quasi-2D materials and compare them to state-of-the-art compounds. 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. |