This is a plugin for OpenMM that allows the ANI Neural Net Potential developed by Smith et al. to be used to compute forces and energies.

This code is released under the MIT license.

At present this plugin must be compiled from source. It uses CMake as its build system.

1. Download the ASE_ANI package for access to the neural network definitions.

2. Acquire modified shared libraries from the ANI authors. Currently a working version of the library for centos is available from the "centos_cuda90_py35" branch of ASE_ANI

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

4. 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.

5. Press "Configure".

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

7. 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.

8. 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". Alternatively, to steps 3-9 you can issue command line statements similar to the following:

mkdir build
cd build
\rm -rf ../build/*

ASE_ANI_DIR=<your path to ASE_ANI installation>
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$ASE_ANI_DIR/lib

condaEnv=~/.conda/envs/openMM2
CUDA_DIR=/local/CUDA/9.0.176
export OPENMM_CUDA_COMPILER=$CUDA_DIR/bin/nvcc

cmake -DOPENMM_DIR=$condaEnv \
      -DCMAKE_INSTALL_PREFIX=$condaEnv \
      -DASE_ANI_DIR=$ASE_ANI_DIR \
      -DCUDA_TOOLKIT_ROOT_DIR=$CUDA_DIR \
      -DNN_BUILD_CUDA_LIB=1 \
       <your path to the openmm-ani source root>

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.

You should now be able to run a minimization of a water molecule by running the command line below:

cd demo
min_ani.py -netDir $ASE_ANI_DIR/ani_models/ani-1ccx_8x -in H2O.pdb -out H2O.min.pdb
min_ani_cell.py -netDir $ASE_ANI_DIR/ani_models/ani-1ccx_8x -in H2O.pdb -out H2O.min.pdb

This runs a very simple minimization of water using OpenMM and the ANI neural Net potential. Pleae note that when starting from a strongly distorted water conformation the minimization might not converge to the expected minimum conformation. This is due to the optimizer taking big steps and landing in regions of the chemical wpace in which the ANI network was not trained. The result is that a wrong local minimum might be found.

Big parts of the is code were derived from the openmm-nn plugin by Peter Eastman. I would like to thank Peter for a great introduction to his plugin and OpenMM development as a whole!

The integration with ANI was done with lots of input from Justin S. Smith. I would like to thank him for all his input and for providing me with a modified version of the ANI shared libraries.

Petr Votava as instrumental in setting up the High Performance Computing enviroment and the OpenMM package at Genentch.

Smith, J. S.; Isayev, O.; Roitberg, A. E. ANI-1: An Extensible Neural Network Potential with DFT Accuracy at Force Field Computational Cost. Chem. Sci. 2017, 8 (4), 3192–3203.

Smith, J. S.; Nebgen, B.; Lubbers, N.; Isayev, O.; Roitberg, A. E. Less Is More: Sampling Chemical Space with Active Learning. The Journal of Chemical Physics 2018, 148 (24), 241733.

Smith, J. s.; Nebgen, B. T.; Zubatyuk, R.; Lubbers, N.; Devereux, C.; Barros, K.; Tretiak, S.; Isayev, O.; Roitberg, A. Outsmarting Quantum Chemistry Through Transfer Learning. figshare 2018.

Eastman, P.; Swails, J.; Chodera, J. D.; McGibbon, R. T.; Zhao, Y.; Beauchamp, K. A.; Wang, L.-P.; Simmonett, A. C.; Harrigan, M. P.; Stern, C. D.; et al. OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics. PLOS Computational Biology 2017, 13 (7), e1005659.

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