Run and install
MLatom calculations can be either run on the MLatom@XACS cloud computing without any installation or MLatom’s code and binary can be downloaded and installed locally, both options are free.
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Table of Contents
Run on the cloud
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After installation (see below), MLatom can be used by either running command with command line options:
or by providing an input file:
MLatom can be run by providing it with the input file. Example:
mlatom [input file]
Install via pip
python3 -m pip install -U MLatom
To download a specific version of MLatom, e.g., 2.0.3:
python3 -m pip install MLatom==2.0.3
Install from a zipped package
Alternatively, you can download a zipped package with MLatomPy (requires Python 3.7+) and a statically compiled binary of
cs.so for Linux systems. These files can be unpacked in any directory and used directly without any modifications to the environment variables etc. You may need to make files executable by using command line option
chmod +x MLatom.py MLatomF cs.so.
You can also add your MLatom path into the
$PATH variable with the command (in bash):
It is convenient to add this line to
Installation instructions for enabling interfaced third-party programs, see below.
To run MLatom provide a path to MLatom.py and the necessary command-line options (see in the next section), i.e. in your terminal type:
$pathToMLatom/MLatom.py [command-line options or the name of an input file with options]
It is recommended to set alias mlatom to
Installation of third-party packages
MLatom provides interfaces to many third-party software packages, but they are (usually) not provided with MLatom. The third-party packages below are optional and can be installed separately to enable specific features. MLatom does require some common Python libraries, like Numpy and PyTorch.
- Install Newton-X (NX, preferably version==2.2 for which our implementations were tested)
export NX=/path/to/Newton-Xto define the
TorchANI is required for calculations with AIQM1 and ANI family of potentials.
1. install Numpy and nightly version of PyTorch (if you do not have them already):
pip install numpy tensorboard
pip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu100/torch_nightly.html
2. install TorchANI:
pip install torchani
Visit https://aiqm.github.io/torchani/ for more info. The latest version of TorchANI used for testing was v2.2, you can install this version by
pip install torchani==2.2 if there are any problems when running with the newest version of TorchANI. The CUDA extension for AEV calculation is not supported for the NN part of AIQM1 and ANI-1ccx now.
Required for DPMD and DeepPot-SE potentials.
1. download installer for DeePMD-kit from GitHub
https://github.com/deepmodeling/deepmd-kit/releases (tested v1.2.2)
2. run installer
3. add environmetal variable
$DeePMDkit that point to the where dp binary is located (
bin/ in your installation directory)
GAP and QUIP
Required for GAP-SOAP potentials.
1. compile QUIP and GAP from source
1.1 install prerequisites
sudo apt-get install gcc gfortran python python-pip libblas-dev liblapack-dev (for system uses apt, do equivalent for your OS)
pip install numpy ase f90wrap
1.2 get source code of QUIP and GAP
git clone --recursive https://github.com/libAtoms/QUIP.git
Get source code of GAP from http://www.libatoms.org/gap/gap_download.html (form-filling required).
Then put source code in
export QUIP_ARCH=linux_x86_64_gfortran_openmp # enable multi-threading, use 'export QUIP_ARCH=linux_x86_64_gfortran' if no OpenMP thus no MT capability
export QUIPPY_INSTALL_OPTS=--user # omit for a system-wide installation
Y for gap or edit
Built binaries are in
2. add environmetal variable
$gap_fit for quip and gap_fit
e.g. export quip='/export/home/fcge/GAP-SOAP/QUIP/build/linux_x86_64_gfortran_openmp/quip'
visit https://libatoms.github.io/GAP/index.html for more info.
Required for PhysNet models.
1. clone form PhysNet‘s GitHub page
git clone https://github.com/MMunibas/PhysNet.git
2. install TensorFlow:
pip install tensorflow
3. if you useTensorFlow v2, you need to execute the command below in PhysNet’s directory to make the scripts compatible with TFv2.
for i in `find . -name '*.py'`; do sed -i -e 's/import tensorflow as tf/import tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()/g' -e 's/import tensorflow as tf/import tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()/g' $i; done
4. add environmetal variable $PhysNet to the directory
Required for GDML and sGDML potentials.
1. install sGDML
pip install sgdml==0.4.4
2. add the path of sGDML binary to environmetal variable
Visit http://quantum-machine.org/gdml/doc/ for more info
Note, in our tests we found that installation is more stable with:
pip install scipy==1.7.1
MNDO program is required to provide the ODM2* part of AIQM1. Alternatively, a (development) version of SCINE Sparrow can be used in the future (see a paper on Sparrow; note that the development version of Sparrow also implements single-point AIQM1 calculations).
The free binary and open-source code of the MNDO program is available from the official distributors of the MNDO code as described at https://mndo.kofo.mpg.de.
After the MNDO program is installed, you need to set the environmental variable pointing to the MNDO executable (typically
mndo99), e.g., in bash:
export mndobin=[path to the executable]/mndo99
dftd4 program is required to provide the D4 part of AIQM1.
The dftd4 program can be obtained as both executable and open-source code. We recommend using dftd4 v3.5.0 (dftd4 v2.5.0 for the MLatom versions earlier than 3.0.1), which can calculate Hessian needed for thermochemical calculations. To install the dftd4 program from source code, please see the README.md file on dftd4 GitHub page for more details.
After the dftd4 program is installed, you need to set the environmental variable pointing to the
dftd4executable, e.g., in bash:
export dftd4bin=[path to the executable]/dftd4
Required for geometry optimizations, freq, TS search, IRC, thermochemistry, and ML-NEA. For some of these tasks, alternatively, ASE can be used, see below.
Our implementation work with both Gaussian 09 and Gaussian 16. It is a commercial program, which can be obtained and installed separately.
To use Gaussian interface, make sure that your environmental variable
GAUSS_EXEDIR points to the right place.
Required for geometry optimizations, freq, and thermochemistry. Alternatively, Gaussian can be used, see above.
The ASE (Atomic Simulation Environment) are Python modules, which can be installed as described on ASE website, i.e.:
pip install ase
To enable hyperopt, please run
pip install hyperopt to install the hyperopt package.