# Tutorial for Kernel Methods chapter

On this page you can find instructions how to follow the examples given in the upcoming book chapter on kernel methods (to be published in 2022).

For examples you need Jupyter with ….

## Download

Download the full data set of with 451 points along the H_{2} dissociation curve. File with internuclear distances in the H_{2} molecule in Å:

File with the full CI energies (calculated with the aug-cc-pV6Z basis set, in Hartree):

Download the file with the UHF/STO-3G energies (in Hartree):

The training indices:

```
72
95
119
143
166
190
214
237
261
285
308
332
356
379
403
427
451
```

## Training KRR with the Gaussian kernel

Download the full data set of with 451 points along the H_{2} dissociation curve. File with internuclear distances in the H_{2} molecule in Å:

File with the reference energies (full CI, calculated with the aug-cc-pV6Z basis set, in Hartree):

Now you can download the indices of the points in the data set to be used as the training and test sets. You can check that all the training points are the same as above.

To train and test the KRR model with the Gaussian kernel using the hyperparameters σ=1 and λ=3.5⋅10^{−13}, you can use the following input file:

estAccMLmodel

XfileIn=R_451.dat

Yfile=E_FCI_451.dat

sampling=user-defined

Ntrain=20 iTrainIn=itrain.dat

iTestIn=itest.dat

YestFile=Eest_gaussian.dat

sigma=1

lambda=3.5e-13

You can check that estimated values saved in file are very close to the reference values for both training and test points.