期刊
CHEMICAL SCIENCE
卷 8, 期 4, 页码 3192-3203出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/c6sc05720a
关键词
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资金
- University of Florida through the Graduate School Fellowship (GSF)
- NIH [GM110077]
- DOD-ONR [N00014-16-1-2311]
- Eshelman Institute for Innovation award
- National Science Foundation (NSF)
- Extreme Science and Engineering Discovery Environment (XSEDE) award [DMR110088]
- National Science Foundation [ACI-1053575]
- U.S. Department of Energy through the LANL/LDRD Program
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.
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