rapmat
Rapid materials discovery TUI tool, based on random crystal generation and machine learning interatomic potentials.
Installation
An Nvidia GPU is highly recommended. Linux is recommended as well, since all backends are currently supported on Linux systems. Conda may be useful.
Linux
Install pytorch<2.10.0 with CUDA support if you have an NVIDIA GPU, otherwise skip this step:
pip install torch==2.9.1 torchvision --index-url https://download.pytorch.org/whl/cu126
Then install rapmat:
# basic install
pip install rapmat
# mattersim support
pip install rapmat[mattersim]
# all calculators at once
pip install rapmat[all-calculators]
Run its TUI:
rapmat
Windows
On Windows, only the UPET and MatterSim (the latter if built with its prerequisites installed) MLIPs are supported.
One way to overcome the Windows limitations is WSL2 -- check the Nvidia or Ubuntu guides.
Install WSL2
-
Install the Nvidia Drivers
-
Install wsl:
wsl.exe --install
- Ensure it's up to date:
wsl.exe --update
- Enter into WSL (Ubuntu is the default distro):
wsl.exe
- Install CUDA Toolkit inside WSL:
wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get -y install cuda-toolkit-13-2
Update the linker paths
NequIP compiles its CUDA kernels and needs the CUDA (stub) libraries on the linker path before installation:
export LIBRARY_PATH=/usr/local/cuda/lib64/stubs:$LIBRARY_PATH
Or, even better, add it to the ~/.bashrc to make it permanent:
echo 'export LIBRARY_PATH=/usr/local/cuda/lib64/stubs:$LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc
Proceed to the linux install guide
VASP
You need to set the path to VASP pseudopotentials:
# ~/vasp/pp/potpaw
export VASP_PP_PATH=~/vasp/pp >> ~/.bashrc
source ~/.bashrc
Usage
Basic concepts
A study defines the system (e.g. Al-O) you are working on and the calculation settings like calculator, forces convergence criterion or pressure.
A run defines a specific formula x [formula units range]: e.g. Al2O3 x 6..8 constituting the unit cell being calculated.
Each run belongs to exactly one study, while a study may have many runs. Runs in one study may overlap, but you can view and perform actions such as deduplication or thickness filtering for only one run at a time.
1 maintainer
Metadata
1 maintainer
Release files for rapmat 0.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| rapmat-0.4.1.tar.gz | 176.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| rapmat-0.4.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 328.4 kB
Release files / rapmat-0.4.1.tar.gz
| Download URL | rapmat-0.4.1.tar.gz |
|---|---|
| Size | 176.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / rapmat-0.4.1-py3-none-any.whl
| Download URL | rapmat-0.4.1-py3-none-any.whl |
|---|---|
| Size | 152.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a1141199845da593cf8421cf744f886d5754505630a8fe70d5b27a4fd86034a8
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 16, 2026.
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