articleChemical ScienceJan 1, 2017DIAMOND OA

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

JSJustin S. SmithOIOlexandr IsayevAEAdrián E. Roitberg

University of Florida · Gainesville Obstetrics & Gynecology · +3 more institutions

PubMed
Indexed inarxivcrossrefdoajpubmed

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…

Citation impact

2,011
total citations
FWCI
65.84
Percentile
100%
References
47
Citations per year

Authors

3

Topics & keywords

Topics
Keywords
  • Artificial neural network
  • Force field (fiction)
  • Extensibility
  • Set (abstract data type)
  • Molecule
  • Quantum
  • Field (mathematics)
  • Computer science
No related works found for this paper.

Funding