Compact Graph Representation of crystal structures using Point-wise Distance Distributions

Balasingham, Jonathan, Zamaraev, Viktor, Kurlin, Vitaliy

arXiv.org Artificial Intelligence 

Use of graphs to represent crystal structures has become popular in recent years as they provide a natural translation from atoms and bonds to nodes and edges. Graphs capture structure, while remaining invariant to the symmetries that crystals display. Several works in property prediction, including those with state-of-the-art results, make use of the Crystal Graph. The present work offers a graph based on Point-wise Distance Distributions which retains symmetrical invariance, decreases computational load, and yields similar or better prediction accuracy on both experimental and simulated crystals.

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