Poster
in
Workshop: From Molecules to Materials: ICLR 2023 Workshop on Machine learning for materials (ML4Materials)

Predicting Density of States via Multi-modal Transformer

Namkyeong Lee · Heewoong Noh · Sungwon Kim · Dongmin Hyun · Gyoung S. Na · Chanyoung Park


Abstract:

The density of states (DOS) is a spectral property of materials, which provides fundamental insights on various characteristics of materials. In this paper, we propose to predict the DOS by reflecting the nature of DOS: DOS determines the general distribution of states as a function of energy. Specifically, we integrate the heterogeneous information obtained from the crystal structure and the energies via multi-modal transformer, thereby modeling the complex relationships between the atoms in the crystal structure, and various energy levels. Extensive experiments on two types of DOS, i.e., phonon DOS and electron DOS, with various real-world scenarios demonstrate the superiority of DOSTransformer.

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