Wrappers for applying kernel ridge regression models using the QMLcode library : http://www.qmlcode.org/

Requirements

• Numpy
• scipy
• scikit-learn
• qml-develop fork (preferably) : https://github.com/dkhan42/qml2 or qml-develop (less functionalities): https://github.com/qmlcode/qml/tree/develop

Usage

The KRR_example.py file contains examples on how to perform cross-validate grid search for finding optimal hyperparameters and how to perform kernel ridge regression for both global and local representations using this wrapper. It also contains an example of using a pre-computed kernel matrix to perform kernel ridge regression which is useful with expensive kernels such as FCHL19.

References

If you found this wrapper useful, please consider citing the following work where it was first implemented :

Danish Khan, Stefan Heinen, O. Anatole von Lilienfeld; Kernel based quantum machine learning at record rate: Many-body distribution functionals as compact representations. J. Chem. Phys. 21 July 2023; 159 (3): 034106. https://doi.org/10.1063/5.0152215