Transferable Machine-Learning Model of the Electron Density

Authors: Andrea Grisafi1,2, Alberto Fabrizio2,3, Benjamin Meyer2,3, David Wilkins1, Clemence Corminboeuf2,3, Michele Ceriotti1,2*

  1. Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
  2. National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
  3. Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
  • Corresponding author email:

DOI10.24435/materialscloud:2019.0076/v1 (version v1, submitted on 28 October 2019)

How to cite this entry

Andrea Grisafi, Alberto Fabrizio, Benjamin Meyer, David Wilkins, Clemence Corminboeuf, Michele Ceriotti, Transferable Machine-Learning Model of the Electron Density, Materials Cloud Archive (2019), doi: 10.24435/materialscloud:2019.0076/v1.


The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. 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. Applications are shown for various hydrocarbon molecules of increasing complexity and flexibility, and demonstrate the accuracy of the model when predicting the density on octane and octatetraene after training exclusively on butane and butadiene. This transferable, data-driven model can be used to interpret experiments, accelerate electronic structure calculations, and compute electrostatic interactions in molecules and condensed-phase systems.

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711.4 KiB Coordinates of 1000 ethene molecules in .xyz format.
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360.6 KiB Coordinates of 1000 ethane molecules in .xyz format.
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701.2 KiB Coordinates of 1000 butadiene molecules in .xyz format.
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912.1 KiB Coordinates of 1000 butane molecules in .xyz format.
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115.8 KiB Coordinates of 100 octatetraene molecules in .xyz format.
MD5MD5: 75f593da7c24649be491839640c39c77
194.5 KiB Coordinates of 100 octane molecules in .xyz format.
MD5MD5: c858fb815155e96e8a7a9e1d4d4e66aa
9.6 GiB Pseudo-valence electron densities of the ethene dataset.
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22.5 GiB Pseudo-valence electron densities of the butane dataset.
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12.7 GiB Pseudo-valence electron densities of the ethane dataset.
MD5MD5: 7d4fff70be4d92bbdb9d8518d2da1fd8
16.0 GiB Pseudo-valence electron densities of the butadiene dataset.
MD5MD5: 4c171fc59c02aa4e7066d2f06a112e6c
2.9 GiB Pseudo-valence electron densities of the octatetraene dataset.
MD5MD5: ac8a4a0460202cdf655aac6200b5d4af
4.2 GiB Pseudo-valence electron densities of the octane dataset.
MD5MD5: 24b17a3a22abc378ee6620c289b32288
888 Bytes README file for data content and format.


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.


MARVEL/DD1 machine learning electron density

Version history

28 October 2019 [This version]