  Progress in both Machine Learning (ML) and Quantum Chemistry (QC) methods
have resulted in high accuracy ML models for QC properties. Datasets such as
MD17 and WS22 have been used to benchmark these models at some level of QC
method, or fidelity, which refers to the accuracy of the chosen QC method.
Multifidelity ML (MFML) methods, where models are trained on data from more
than one fidelity, have shown to be effective over single fidelity methods.
Much research is progressing in this direction for diverse applications ranging
from energy band gaps to excitation energies. One hurdle for effective research
here is the lack of a diverse multifidelity dataset for benchmarking. We
provide the quantum Chemistry MultiFidelity (CheMFi) dataset consisting of five
fidelities calculated with the TD-DFT formalism. The fidelities differ in their
basis set choice: STO-3G, 3-21G, 6-31G, def2-SVP, and def2-TZVP. CheMFi offers
to the community a variety of QC properties such as vertical excitation
properties and molecular dipole moments, further including QC computation times
allowing for a time benefit benchmark of multifidelity models for ML-QC.
