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Modern machine learning techniques have been extensively applied to materialsscience, especially for property prediction tasks. A majority of these methodsaddress scalar property predictions, while more challenging spectral propertiesremain less emphasized. We formulate a crystal-to-sequence learning task andpropose a novel attention-based learning method, Xtal2DoS, which decodes thesequential representation of the material density of states (DoS) properties byincorporating the learned atomic embeddings through attention networks.Experiments show Xtal2DoS is faster than the existing models, and consistentlyoutperforms other state-of-the-art methods on four metrics for two fundamentalspectral properties, phonon and electronic DoS.

Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction