Rapid discovery of stable materials by coordinate-free coarse graining

Rhys Goodall, Abhijith S Parackal, Felix Andreas Faber, Rickard Armiento, Alpha Lee

Abstract:

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 bottlenecked by crystal structure identification
when investigating previously unidentified 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 finding unknown theoretically stable
materials, identifying 1569 materials that lie below the known convex hull of previously calculated materials from just
5675 ab initio calculations. Our approach opens up fundamental advances in computational materials discovery.