Additionally, the high computational efficiency of the xTB methods, in combination with the ALPB model, gave rise to the creation of physics-informed machine learning models utilizing features or training data obtained with the xtb program. One example of an approach that uses readily available features for the calculation of solvation free energies is the creation of the implicitly perturbed Hamiltonian. This approach defines energy attributes of the molecular Hamiltonian that are implicitly perturbed by solvents, creating a new class of molecular representations for deep learning approaches.