# # CDDL HEADER START # # The contents of this file are subject to the terms of the Common Development # and Distribution License Version 1.0 (the "License"). # # You can obtain a copy of the license at # http://www.opensource.org/licenses/CDDL-1.0. See the License for the # specific language governing permissions and limitations under the License. # # When distributing Covered Code, include this CDDL HEADER in each file and # include the License file in a prominent location with the name LICENSE.CDDL. # If applicable, add the following below this CDDL HEADER, with the fields # enclosed by brackets "[]" replaced with your own identifying information: # # Portions Copyright (c) [yyyy] [name of copyright owner]. All rights reserved. # # CDDL HEADER END # # # Copyright (c) 2019, Regents of the University of Minnesota. # # Contributors: # Mingjian Wen # 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.