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A Transferable Machine-Learning Model of the Electron Density

submitted on 17.09.2018, 09:23 and posted on 18.09.2018, 15:30 by Clemence Corminboeuf, Michele Ceriotti, Benjamin Meyer, Alberto Fabrizio, Andrea Grisafi, David M. Wilkins

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.


National Centre of Competence in Research (NCCR) Materials Revolution: Computational Design and Discovery of Novel Materials (MARVEL) of the Swiss National Science Foundation (SNSF) and the European Research Council under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 677013-HBMAP)


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Ecole Polytechnique Federale de Lausanne



ORCID For Submitting Author


Declaration of Conflict of Interest

The authors have no conflict of interest.