  A fundamental challenge in materials science pertains to elucidating the
relationship between stoichiometry, stability, structure, and property. Recent
advances have shown that machine learning can be used to learn such
relationships, allowing the stability and functional properties of materials to
be accurately predicted. However, most of these approaches use atomic
coordinates as input and are thus bottle-necked by crystal structure
identification when investigating novel materials. Our approach solves this
bottleneck by coarse-graining the infinite search space of atomic coordinates
into a combinatorially enumerable search space. The key idea is to use Wyckoff
representations -- coordinate-free sets of symmetry-related positions in a
crystal -- as the input to a machine learning model. Our model demonstrates
exceptionally high precision in discovering new theoretically stable materials,
identifying 1,569 materials that lie below the known convex hull of previously
calculated materials from just 5,675 ab-initio calculations. Our approach opens
up fundamental advances in computational materials discovery.
