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# Copyright (c) 2019, Regents of the University of Minnesota.
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# Contributors:
#    Mingjian Wen
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This directory contains a model driver for the hybrid neural network (NN) and
Lennard-Jones (LJ) potential.

1. Two parameter files are needed, one for the LJ part and the other for the NN
   part.

   For the LJ part, the following parameters should be provided in a single
   line:
   species  A  r_up_min  r_up_max  r_down_min  r_down_max  cutoff

   The NN parameters is automatically generated by KLIFF (https://kliff.readthedocs.io).
   See KLIFF documentation for the format.

2. The following files are included in this directory:

   ANN.hpp, ANN.cpp:
      implementation wrapper of the driver
   ANNImplementation.hpp, ANNImplementation.cpp:
      implementation of the driver
   descriptor.h, descriptor.cpp:
      atomic environment descriptor
   network.h, network.cpp:
      feed-forward neural network
   helper.hpp, helper.cpp:
      help functions to manage memory and compute virial
   eigen-v3.3.7.tar.xz:
      the Eigen matrix operation library
   ANNImplementationComputeDispatch.cpp
      helper function


References:

1. M. Wen and E. B. Tadmor, "A hybrid machine learning and physics-based
   interatomic potential for multilayer graphene structures", Phys. Rev. B,
   submitted, 2019.
