  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 fully transferable potential for organic
molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular
Energies) or ANI in short. ANI is a new method and procedure for training
neural network potentials that utilizes a highly modified version of the Behler
and Parrinello symmetry functions to build single-atom atomic environment
vectors as a molecular representation. We utilize ANI to build a potential
called ANI-1, which was trained on a subset of the GDB databases with up to 8
heavy atoms 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 propose a Normal Mode
Sampling (NMS) method for generating molecular configurations. 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, with root mean square errors as
low as 0.56 kcal/mol.
