ShiftML

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.

Usage

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)

IMPORTANT: Install pre-instructions before PiPy release

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

Installation

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 .

The code that makes it work

This project would not have been possible without the following packages:

• Metadata and model handling: metatensor
• Atomic descriptor engine: rascaline

Documentation

The documentation is available here.

Contributors

Matthias Kellner
Yuxuan Zhang
Ruben Rodriguez Madrid
Guillaume Fraux

References

This package is based on the following papers:

• Chemical shifts in molecular solids by machine learning - Paruzzo et al. [1]
• A Bayesian approach to NMR crystal structure determination - Engel et al. [2]
• A Machine Learning Model of Chemical Shifts for Chemically and
  Structurally Diverse Molecular Solids - Cordova et al. [3]