Installation ============ .. _installation: Installation of Linux System Configuration ------------------------------------------ Debian-based distributions (also Windows 11 with WSL) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ You can also use the same commands on a pure Debian-based Linux system or Windows systems with WSL. If you do not know how to install Linux on Windows 11 with WSL, you can view `this video `_. On the WSL system, you can use either Debian or Ubuntu. We recommend Ubuntu due to the support provided by Microsoft. First, install the required system files: .. code-block:: console $ sudo apt update && sudo apt upgrade -y $ sudo apt install -y python3-venv python3-pip unzip python-is-python3 \ python3-dev libopenblas-dev libxc-dev libscalapack-mpi-dev \ libfftw3-dev libkim-api-dev openkim-models libkim-api2 pkg-config \ task-spooler Fedora-based distributions ^^^^^^^^^^^^^^^^^^^^^^^^^^ First, install the required system files: .. code-block:: console $ sudo dnf update $ sudo dnf install python3-devel openblas-devel libxc-devel scalapack-openmpi-devel fftw-devel pkgconf You also must install `kim-api`, `kim-api-devel`, and `openkim-models`. At the time of writing these instructions, packages for Fedora 43 cannot be installed remotely. Therefore, we must download them, then install them with dnf locally. The order is important: .. code-block:: console $ wget https://download.copr.fedorainfracloud.org/results/lecris/cmake-ninja/fedora-rawhide-x86_64/08840866-kim-api/kim-api-2.2.1-11.fc43.x86_64.rpm $ wget https://download.copr.fedorainfracloud.org/results/lecris/cmake-ninja/fedora-rawhide-x86_64/08840866-kim-api/kim-api-devel-2.2.1-11.fc43.x86_64.rpm $ wget https://download.copr.fedorainfracloud.org/results/lecris/cmake-ninja/fedora-rawhide-x86_64/08841484-openkim-models/openkim-models-2021.01.28-12.fc43.src.rpm $ sudo dnf install kim-api-2.2.1-11.fc43.x86_64.rpm $ sudo dnf install kim-api-devel-2.2.1-11.fc43.x86_64.rpm $ sudo dnf install openkim-models-2021.01.28-12.fc43.src.rpm Python Virtual Environment Installation --------------------------------------- Then, if you do not have a Python environment, create one and activate it: .. code-block:: console $ python -m venv ~/.venv_nw $ source ~/.venv_nw/bin/activate Python Libraries Installation ----------------------------- For `dftsolve`, `mdsolve`, and `mlsolve` commands, we need to install many Python libraries. ASE and GPAW for dftsolve ^^^^^^^^^^^^^^^^^^^^^^^^^ After preparing your Linux system and environment, you must have the `ase` and `gpaw` codes on your computer. You can find more information about installing `ASE `_ and `GPAW `_ from their related sites. .. code-block:: console (.venv_nw) $ pip3 install --upgrade ase (.venv_nw) $ pip3 install setuptools_scm (.venv_nw) $ pip3 install spglib docutils elastic requests phonopy pyrapl pymongo pandas Creating a `siteconfig.py` file is important. You can use any text editor. Here, we are creating a file with the cat command, writing necessary information inside it, then closing it with the Ctrl-D command (^D). .. code-block:: console (.venv_nw) $ mkdir -p ~/.gpaw (.venv_nw) $ cat > ~/.gpaw/siteconfig.py fftw = True scalapack = True libraries = ['xc', 'blas', 'fftw3', 'scalapack-openmpi'] ^D If you have problems with libraries fftw, scalapack, you can remove them from `siteconfig.py` file. They are simply optional. Then continue to install gpaw .. code-block:: console (.venv_nw) $ pip3 install --upgrade gpaw Use `gpaw info` to see installation information. However, PAW datasets are not installed yet. To install them, first create a directory under `~/.gpaw` and then install PAW datasets. .. code-block:: console (.venv_nw) $ mkdir ~/.gpaw/gpaw-setups (.venv_nw) $ gpaw install-data --gpaw ~/.gpaw/gpaw-setups/ Installation of ASAP and KIM for mdsolve ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For quick optimization, we need simple interatomic modeling. For this, we need `ASAP3 `_ for ASE, then we must use `KIM `_ with `OpenKIM `_ models and `kimpy `_ libraries. .. code-block:: console (.venv_nw) $ pip3 install --upgrade asap3 (.venv_nw) $ pip3 install --upgrade kimpy Installation of Machine Learning libraries for mlsolve ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For machine learning features, we must install the libraries related with `PyTorch `_, `MACE (Multi-Atomic Cluster Expansion) `_, `CHGNet (Charge-Informed Graph Neural Network) `_, `SevenNet (Scalable Equivariance Enabled Neural Network) `_: .. code-block:: console (.venv_nw) $ pip3 install torch mace-torch chgnet sevenn Installation of Nanoworks ------------------------- Then, lastly, install nanoworks: .. code-block:: console (.venv_nw) $ pip3 install nanoworks