  Multifidelity machine learning (MFML) for quantum chemical (QC) properties
has seen strong development in the recent years. The method has been shown to
reduce the cost of generating training data for high-accuracy low-cost ML
models. In such a set-up, the ML models are trained on molecular geometries and
some property of interest computed at various computational chemistry
accuracies, or fidelities. These are then combined in training the MFML models.
In some multifidelity models, the training data is required to be nested, that
is the same molecular geometries are included to calculate the property across
all the fidelities. In these multifidelity models, the requirement of a nested
configuration restricts the kind of sampling that can be performed while
selection training samples at different fidelities.
  This work assesses the use of non-nested training data for two of these
multifidelity methods, namely MFML and optimized MFML (o-MFML). The assessment
is carried out for the prediction of ground state energies and first vertical
excitation energies of a diverse collection of molecules of the CheMFi dataset.
Results indicate that the MFML method still requires a nested structure of
training data across the fidelities. However, the o-MFML method shows promising
results for non-nested multifidelity training data with model errors comparable
to the nested configurations.
