  Conventional wisdom of materials modelling stipulates that both chemical
composition and crystal structure are integral in the prediction of physical
properties. However, recent developments challenge this by reporting accurate
property-prediction machine learning (ML) frameworks using composition alone
without knowledge of the local atomic environments or long-range order. To
probe this behavior, we conduct a systematic comparison of supervised ML models
built on composition only vs. composition plus structure features. Similar
performance for property prediction is found using both models for compounds
close to the thermodynamic convex hull. We hypothesize that composition embeds
structural information of ground-state structures in support of
composition-centric models for property prediction and inverse design of stable
compounds.
