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OpenMM Neural Network Plugin

This is a plugin for OpenMM that allows neural networks to be used for defining forces. It is implemented with TensorFlow. To use it, you create a TensorFlow graph that takes particle positions as input and produces forces and energy as output. This plugin uses the graph to apply forces to particles during a simulation.

Installation

At present this plugin must be compiled from source. It uses CMake as its build system. Before compiling you must install the TensorFlow C API by following the instructions at https://www.tensorflow.org/install/install_c. You can then follow these steps.

  1. Create a directory in which to build the plugin.

  2. Run the CMake GUI or ccmake, specifying your new directory as the build directory and the top level directory of this project as the source directory.

  3. Press "Configure".

  4. Set OPENMM_DIR to point to the directory where OpenMM is installed. This is needed to locate the OpenMM header files and libraries.

  5. Set TENSORFLOW_DIR to point to the directory where you installed the TensorFlow C API.

  6. Set CMAKE_INSTALL_PREFIX to the directory where the plugin should be installed. Usually, this will be the same as OPENMM_DIR, so the plugin will be added to your OpenMM installation.

  7. If you plan to build the OpenCL platform, make sure that OPENCL_INCLUDE_DIR and OPENCL_LIBRARY are set correctly, and that NN_BUILD_OPENCL_LIB is selected.

  8. If you plan to build the CUDA platform, make sure that CUDA_TOOLKIT_ROOT_DIR is set correctly and that NN_BUILD_CUDA_LIB is selected.

  9. Press "Configure" again if necessary, then press "Generate".

  10. Use the build system you selected to build and install the plugin. For example, if you selected Unix Makefiles, type make install to install the plugin, and make PythonInstall to install the Python wrapper.

Usage

The first step is to create a TensorFlow graph defining the calculation to perform. It should have an input called positions which will be set to the particle positions. It should produce two outputs: one called forces containing the forces to apply to the particles, and one called energy with the potential energy. This graph must then be saved to a binary protocol buffer file. Here is an example of Python code that does this for a very simple calculation (a harmonic force attracting every particle to the origin).

import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
    positions = tf.placeholder(tf.float32, [None, 3], 'positions')
    energy = tf.reduce_sum(positions**2, name='energy')
    forces = tf.identity(tf.gradients(-energy, positions), name='forces')
tf.train.write_graph(graph, '.', 'graph.pb', as_text=False)

The data types of the graph's input and output tensors may be either float32 or float64.

To use the graph in a simulation, create a NeuralNetworkForce object and add it to your System. The constructor takes the path to the saved graph as an argument. For example,

from openmmnn import *
f = NeuralNetworkForce('graph.pb')
system.addForce(f)

Alternatively, in Python (but not C++) you can directly pass a tf.Graph to the constructor without writing it to a file first:

f = NeuralNetworkForce(graph)

If the graph includes any variables, pass a tf.Session as the second argument. It will use TensorFlow's freeze_graph utility to create a frozen version of the graph in which variables have been replaced with values taken from the session:

f = NeuralNetworkForce(graph, session)

When defining the graph to perform a calculation, you may want to apply periodic boundary conditions. To do this, call setUsesPeriodicBoundaryConditions(True) on the NeuralNetworkForce. The graph is then expected to contain an input called boxvectors which will contain the current periodic box vectors. You can make use of them in whatever way you want for computing the force. For example, the following code applies periodic boundary conditions to each particle position to translate all of them into a single periodic cell.

positions = tf.placeholder(tf.float32, [None, 3], 'positions')
boxvectors = tf.placeholder(tf.float32, [3, 3], 'boxvectors')
boxsize = tf.diag_part(boxvectors)
periodicPositions = positions - tf.floor(positions/boxsize)*boxsize

Note that this code assumes a rectangular box. Applying periodic boundary conditions with a triclinic box requires a slightly more complicated calculation.

License

This is part of the OpenMM molecular simulation toolkit originating from Simbios, the NIH National Center for Physics-Based Simulation of Biological Structures at Stanford, funded under the NIH Roadmap for Medical Research, grant U54 GM072970. See https://simtk.org.

Portions copyright (c) 2018 Stanford University and the Authors.

Authors: Peter Eastman

Contributors:

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS, CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.