Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN). (arXiv:2201.08348v1 [cond-mat.mtrl-sci])

Machine learning (ML) based models have greatly enhanced the traditional
materials discovery and design pipeline. Specifically, in recent years,
surrogate ML models for material property prediction have demonstrated success
in predicting discrete scalar-valued target properties to within reasonable
accuracy of their DFT-computed values. However, accurate prediction of spectral
targets such as the electron Density of States (DOS) poses a much more
challenging problem due to the complexity of the target, and the limited amount
of available training data. In this …

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