While the classical training set is well suited for the training of molecules, there are some restrictions on integrating surface properties. Depending on the ReaxFF parameter set, the lattice constant of the bulk phases may vary with respect to the reference data. Yet, the minimum lattice constant change is not taken into account for the surface structures within the training set. In this way, the introduced lattice strain can influence fundamental surface quantities and thus may distort the error function. Moreover, due to the lack of constraint options, the description of metastable or unstable surface sites is limited. In a classical training set, the images of a minimum energy path or the data of a potential energy scan are typically included in the form of a single point calculation or by applying bond, angle, or torsion restraints. However, an accurate representation of the potential energy surface (PES) is crucial for the description of the surface kinetics.