Predicting materials properties without crystal structure: Deep representation learning from stoichiometry

Code

• github.com/CompRhys/roost
  OfficialIn paperpytorch★ 60
• github.com/s-a-malik/inorg-synth-graph
  pytorch★ 0
• github.com/CompRhys/aviary
  pytorch★ 0

Abstract

Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typically use descriptors constructed from knowledge of either the full crystal structure -- therefore only applicable to materials with already characterised structures -- or structure-agnostic fixed-length representations hand-engineered from the stoichiometry. We develop a machine learning approach that takes only the stoichiometry as input and automatically learns appropriate and systematically improvable descriptors from data. Our key insight is to treat the stoichiometric formula as a dense weighted graph between elements. Compared to the state of the art for structure-agnostic methods, our approach achieves lower errors with less data.

Tasks

Reproductions