What Information is Necessary and Sufficient to Predict Materials Properties using Machine Learning?

Understand

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.