(1) Black-box potentials. Typically these methods rely on the bottom-up approach, where a ML algorithm is trained with QM data of small molecules to extract a description of immediate environments of atoms, and to predict the atom-wise energies and forces. Such an algorithm is free from the shackles of the fixed functional forms of the classical force fields and is also intrinsically polarizable. However, it is somewhat limited in describing longrange interactions that cannot be cut off, such as electrostatics. Perhaps the most prominent examples have been those using neural networks, pioneered by Behler and Parrinello, 113 after which several architectures, such as ANI and SchNet 116 have emerged.