\texttt{UMA-DOS}: Transfer Learning from Pretrained Atomistic Models for Density of States Prediction

The Density Of States (DOS) is a fundamental spectral property of crystalline materials that governs a wide range of chemical, electronic, and optical properties. Despite its importance, fast but accurate DOS prediction based on machine learning remains challenging because it involves a high-dimensional structured target, the physically meaningful features of which depend on subtle relations between crystal structure and energy-resolved electronic states. Existing machine-learning approaches primarily focus on learning strong structural representations from crystal graphs, but often rely on DOS supervision alone and do not fully exploit the transferable information already learned by large pretrained atomistic models. In this work, we present \texttt{UMA-DOS}, a transfer-learning framework for total DOS prediction built on pretrained UMA backbones. \texttt{UMA-DOS} uses a pretrained atomistic encoder to represent crystal structure, aggregates node-level features into a graph-level representation, and maps this representation to an energy-resolved DOS through a structured spectral decoder with explicit shape--amplitude factorization. Under a unified Materials Project evaluation pipeline, \texttt{UMA-DOS} achieves the strongest controlled performance among the reproduced baselines, with its clearest advantages appearing in shape-sensitive measures of spectral fidelity. Transfer ablations further show that the value of pretraining is realized only through downstream adaptation: frozen transfer underperforms scratch training, whereas end-to-end fine-tuning yields the best results. These findings show that pretrained atomistic representations can transfer effectively to total DOS prediction when paired with a shape-aware spectral decoder and adapted end-to-end.