Transferable Machine-Learning Model of the Electron Density.

Journal: ACS central science
Published Date: Dec 26, 2018

Abstract

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.

Authors

  • Andrea Grisafi
    Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland michele.ceriotti@epfl.ch.
  • Alberto Fabrizio
    Laboratory for Computational Molecular Design , Institute of Chemical Sciences and Engineering , École Polytechnique Fédérale de Lausanne , CH-1015 Lausanne , Switzerland . Email: clemence.corminboeuf@epfl.ch.
  • Benjamin Meyer
    Laboratory for Computational Molecular Design , Institute of Chemical Sciences and Engineering , École Polytechnique Fédérale de Lausanne , CH-1015 Lausanne , Switzerland . Email: clemence.corminboeuf@epfl.ch.
  • David M Wilkins
    Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
  • Clemence Corminboeuf
    Laboratory for Computational Molecular Design , Institute of Chemical Sciences and Engineering , École Polytechnique Fédérale de Lausanne , CH-1015 Lausanne , Switzerland . Email: clemence.corminboeuf@epfl.ch.
  • Michele Ceriotti
    Laboratory of Computational Science and Modeling (COSMO), École Polytechnique Fédérale de Lausanne Lausanne Switzerland.

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