James Rondinelli1
Northwestern University1
Functional inorganic electronic materials design has undergone a shift from chemical-intuition-based strategies to data-driven synthesis and simulation. Numerous machine learning models have been developed to successfully predict materials properties and generate new crystal structures. Many existing approaches, however, rely upon physical insights to construct handcrafted features (descriptors), which are not always readily available. For novel materials with sparse prior data and insufficient physical understanding, conventional machine learning models may display limited predictability and are often applied to known structure types. In this talk, I will address this challenge by introducing 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, I will showcase our workflow which combines structure complement analysis, a transparent machine learning model, and high throughput density functional theory (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. Finally, I propose how this 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.