Installation

Installation of mlp-train package requires conda or mamba. If you do not have it already installed, you can download it from https://www.anaconda.com/docs/getting-started/miniconda/install#macos-linux-installation.

Mlp-train can be cloned from https://github.com/duartegroup/mlp-train.

git clone https://github.com/duartegroup/mlp-train.git

Each machine learning potential (MLP) has its own conda environment and can be installed by executing corresponding script:

#Install ACE
./install_ace.sh

#Install MACE
./install_mace.sh

ACE installation requires Julia (v<=1.6) in the $PATH.

MACE potential benefits from gpu acceleration. To make sure pytorch is installed with CUDA support, you need to either install the environment from machine with GPU access or specify:

CONDA_OVERRIDE_CUDA="11.8" ./install_mace.sh

The packages are installed into a new conda environments. For MACE, the environment is called mlptrain-mace. To activate it, type:

conda activate mlptrain-mace

After activation, you can check that the packages are installed with support for CUDA as:

conda list | grep pytorch

If everything works correctly, you should see something similar to

pytorch  2.4.1 cuda118_py39ha48351b_305 conda-forge

If the third column does not contain the word cuda, you need to install the environment again.