NNs rely on composition of multiple nonlinear functions with highly parameterized linear mappings (layers). The universal approximation theorem colloquially states that NNs of sufficient capacity are (at least in theory) able to approximate virtually any function to any desired accuracy on a given region . For the use case at hand this means that the functional dependence between equilibrium and initial composition of a chemical system (and many other deterministic dependencies) can be captured by neural networks .