Since hyperparameters are not optimized automatically during the training of an ML model, a process called 𝐾-fold stratified cross-validation (𝐾-fold CV) is applied in ArchOnML to de-termine the best set of all 𝜎 𝑔 and 𝜆. In short, the notebook performs a number of 𝐾 grid-based hyperparameter optimizations, where the user provides the initial inputs on how grid is to be set up. Note though, that ArchOnML uses a special approach to deal with the grid dimensions relating to each of the descriptors 𝜎 𝑔 , specifically. In the previous work, it was already shown that it is possible to reduce the grid optimization problem of 1 + 𝑁 descriptors dimensions into two dimensions only. Here, one dimension is allocated for 𝜆, and a second dimension is given by σ, which is a global scaling factor applied to a set of initial guesses for all individual 𝜎 𝑔 , simultaneously. However, while this greatly reduced the calculation time for hyperparameter optimization, the initial values to scale by σ for each of the 𝜎 𝑔 still needed to be provided manually.