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A Transferable Machine-Learning Model of the Electron Density

preprint
submitted on 17.09.2018 and posted on 18.09.2018 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.

Funding

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)

History

Email Address of Submitting Author

clemence.corminboeuf@epfl.ch

Institution

Ecole Polytechnique Federale de Lausanne

Country

Switzerland

ORCID For Submitting Author

0000-0001-7993-2879

Declaration of Conflict of Interest

The authors have no conflict of interest.

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