Transferable Machine-Learning Model of the Electron Density

Andrea Grisafi, Alberto Fabrizio, Benjamin Meyer, David M. Wilkins, Clemence Corminboeuf, Michele Ceriotti*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

92 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)57-64
Number of pages8
JournalACS Central Science
Issue number1
Early online date26 Dec 2018
Publication statusPublished - 23 Jan 2019
Externally publishedYes


  • Machine Learning
  • Density Functional Theory
  • Gaussian Process Regression

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)


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