Innovative solar system missions must become increasingly innovative and elaborate since "the low-hanging fruits have already been picked." Solar sails, which are propelled solely by solar radiation pressure, are among the key technologies for the future exploration of the solar system because they make missions possible that would otherwise be infeasible due to their immense propellant requirements. The optimization of solar sail trajectories, however, is a difficult task. In the talk, a method is presented that is based on machine learning, fusing artificial neural networks and evolutionary algorithms. Such optimization methods may also be applied for subsurface ice melting probes, as they are required to explore Jupiter's and Saturn's icy moons, which may harbor life in the oceans beneath their thick ice crusts. Such ice melting probes have been developed at FH Aachen and successfully tested in Antarctic ice. It will be interesting to discuss whether those methods can also be applied in polymer research.