Andrea Grisafi, Alberto Fabrizio, Benjamin Meyer, D. Wilkins, C. Corminboeuf, M. Ceriotti
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A Transferable Machine-Learning Model of the Electron Density
We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost.
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