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

Understand

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.