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Issue 4, 2017
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ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

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Abstract

Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set.

Graphical abstract: ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

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Publication details

The article was received on 31 Dec 2016, accepted on 07 Feb 2017 and first published on 08 Feb 2017


Article type: Edge Article
DOI: 10.1039/C6SC05720A
Citation: Chem. Sci., 2017,8, 3192-3203
  • Open access: Creative Commons BY-NC license
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    ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

    J. S. Smith, O. Isayev and A. E. Roitberg, Chem. Sci., 2017, 8, 3192
    DOI: 10.1039/C6SC05720A

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