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Machine learning for materials discovery has largely focused on predicting anindividual scalar rather than multiple related properties, where spectralproperties are an important example. Fundamental spectral properties includethe phonon density of states (phDOS) and the electronic density of states(eDOS), which individually or collectively are the origins of a breadth ofmaterials observables and functions. Building upon the success of graphattention networks for encoding crystalline materials, we introduce aprobabilistic embedding generator specifically tailored to the prediction ofspectral properties. Coupled with supervised contrastive learning, ourmaterials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods forpredicting ab initio phDOS and eDOS for crystalline materials. We demonstrateMat2Spec's ability to identify eDOS gaps below the Fermi energy, validatingpredictions with ab initio calculations and thereby discovering candidatethermoelectrics and transparent conductors. Mat2Spec is an exemplar frameworkfor predicting spectral properties of materials via strategically incorporatedmachine learning techniques.

Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings