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arXiv · 2206.04968

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

Siyu Isaac Parker Tian·Aron Walsh·Zekun Ren·Qianxiao Li·Tonio Buonassisi
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Abstract

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.

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Siyu Isaac Parker Tian, Aron Walsh, Zekun Ren, Qianxiao Li, Tonio Buonassisi. 2022-06-10. What Information is Necessary and Sufficient to Predict Materials Properties using Machine Learning?. https://arxiv.org/abs/2206.04968

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