ShiftML2
Creators
- 1. Laboratory of Magnetic Resonance, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
- 2. Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom.
- 3. Laboratory of Computational Science and Modelling, Institute of Materials, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
Description
ShiftML2: A machine learning model of chemical shifts for chemically and structurally diverse molecular solids
This record contains the ShiftML2 Python package.
Installation
First, download all files, unzip the "ShiftML2.zip" archive, and place the model files for the elements you want to predict (.pk files) in the ShiftML2/models directory.
A script is available to create a Python environment containing all necessary packages to run ShiftML2. To install it, run the file:
./install_env.shIf you already have a python environment containing the required packages, you can directly install the ShiftML2 package using:
pip install .You can then test the installation by running the "example.ipynb" notebook.
Additional files
We also provide the complete initial and final (after outlier removal and FPS selection) training set, as well as the test set with ShiftML2 predictions in the files "initial_training_test_set.zip" and "final_training_test_set.zip".
The training scripts are provided as jupyter notebooks in the file "training_scripts.zip".
Files
final_training_test_sets.zip
Files
(19.1 GB)
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Additional details
Funding
- Swiss National Science Foundation
- Extending NMR crystallography to complex non-crystalline materials 200020_178860