Rapid discovery of stable materials by coordinatefree coarse graining
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 coarsegraining the infinite search space of atomic coordinates into a combinatorially enumerable search space. The key idea is to use Wyckoff representations, coordinatefree sets of symmetryrelated 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. Coordinatefree machine learning models enable the efficient prediction of unknown stable materials.
 Publication:

Science Advances
 Pub Date:
 July 2022
 DOI:
 10.1126/sciadv.abn4117
 arXiv:
 arXiv:2106.11132
 Bibcode:
 2022SciA....8N4117G
 Keywords:

 Condensed Matter  Materials Science;
 Physics  Computational Physics
 EPrint:
 Code Available: https://github.com/CompRhys/aviary