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The density of states (DOS) is a spectral property of crystalline materials,which provides fundamental insights into various characteristics of thematerials. While previous works mainly focus on obtaining high-qualityrepresentations of crystalline materials for DOS prediction, we focus onpredicting the DOS from the obtained representations by reflecting the natureof DOS: DOS determines the general distribution of states as a function ofenergy. That is, DOS is not solely determined by the crystalline material butalso by the energy levels, which has been neglected in previous works. In thispaper, we propose to integrate heterogeneous information obtained from thecrystalline materials and the energies via a multi-modal transformer, therebymodeling the complex relationships between the atoms in the crystallinematerials and various energy levels for DOS prediction. Moreover, we propose toutilize prompts to guide the model to learn the crystal structuralsystem-specific interactions between crystalline materials and energies.Extensive experiments on two types of DOS, i.e., Phonon DOS and Electron DOS,with various real-world scenarios demonstrate the superiority ofDOSTransformer. The source code for DOSTransformer is available athttps://github.com/HeewoongNoh/DOSTransformer.

Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer