The development code in master is designed to be more modular (which was the original intent of splitting Amp from Neural), and to allow easier integration with approaches like that of TensorFlow. Some features, like parallelization and backpropagation, are still being fully implemented in this branch. For the latest stable version, see v0.4.1.
Developed by Andrew Peterson & Alireza Khorshidi, Brown University School of Engineering. Amp allows for the modular representation of the potential energy surface with descriptor and regression methods of choice for the user.
This project lives at: https://bitbucket.org/andrewpeterson/amp
Documentation lives at: http://amp.readthedocs.org
(This project was formerly known as "Neural". The last stable version of Neural can be found at https://bitbucket.org/andrewpeterson/neural)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Amp works in pure python, however, it will be annoyingly slow unless the associated Fortran 90 modules are compiled which will speed up several parts of the codes. The compilation of the Fortran 90 code and integration with the python parts is accomplished with f2py, which is part of NumPy. A Fortran 90 compiler will also be necessary on the system; a reasonable open-source option is GNU Fortran, or gfortran. This compiler will generate Fortran modules (.mod). gfortran will also be used by f2py to generate extension module fmodules.so on Linux or fmodules.pyd on Windows. In order to prepare the extension module the following steps need to be taken:
1- Compile model Fortran subroutines inside the model folder by:
$ cd ~/path/to/my/codes/model
$ gfortran -c neuralnetwork.f90
2- Move the module ``neuralnetwork.mod'' created in the last step, to the parent directory by:
$ mv neuralnetwork.mod ../
3- Go back to the parent directory and compile the model Fortran subroutines in companion with the descriptor and neuralnetwork subroutines by something like:
$ cd ../
$ f2py -c -m fmodules model.f90 descriptor/gaussian.f90 descriptor/zernike.f90 model/neuralnetwork.f90
or on a Windows machine by:
$ f2py -c -m fmodules model.f90 descriptor/gaussian.f90 descriptor/zernike.f90 model/neuralnetwork.f90 --fcompiler=gnu95 --compiler=mingw32
If the version of fmodules.f90 is not updated, an exception will be raised which tells user which version number should be employed.