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.
A Transferable Machine-Learning Model of the Electron Density
18 September 2018, Version 1
This content is a preprint and has not undergone peer review at the time of posting.