Disclaimer: This package is still under development and should be used with caution.
Welcome to ShitML, a python package for the prediction of chemical shieldings of organic solids and beyond.
Use ShiftML with the atomsitic simulation environment to obtain fast estimates of chemical shieldings:
from ase.build import bulk
from shiftml.ase import ShiftML
frame = bulk("C", "diamond", a=3.566)
calculator = ShiftML("ShiftML1.0")
cs_iso = calculator.get_cs_iso(frame)
print(cs_iso)
Rascaline-torch, one of the main dependence of ShiftML, requires CXX and Rust compilers to be built from source. Most systems come already with configured C/C++ compilers (make sure that some environment variables CC and CXX are set and gcc can be found), but Rust typically needs to be installed manually. For ease of use we strongly recommend to use some sort of package manager to install Rust, such as conda and a fresh environment.
conda create -n shiftml python=3.10
conda activate shiftml
conda install -c conda-forge rust
To install ShiftML, you can use clone this repository and install it using pip, a pipy release will follow soon:
pip install --extra-index-url https://download.pytorch.org/whl/cpu .
This project would not have been possible without the following packages:
- Metadata and model handling: metatensor
- Atomic descriptor engine: rascaline
The documentation is available here.
Matthias Kellner
Yuxuan Zhang
Ruben Rodriguez Madrid
Guillaume Fraux
This package is based on the following papers: