We explore artificial spin ice (ASI) as a substrate for material computation . ASI consists of large numbers of nanomagnets arranged in a 2D lattice. Local interactions between the magnets gives rise to a range of complex collective behavior. The ferromagnets form large networks of nonlinear nodes, which in many ways resemble artificial neural networks. In this work, we investigate key computational properties of ASI through micromagnetic simulations. Our nanomagnetic system exhibits a large number of reachable stable states and a wide range of available dynamics when perturbed by an external magnetic field. Furthermore, we find that the system is able to store and process temporal input patterns. The emergent behavior is highly tunable by varying the parameters of the external field. Our findings highlight ASI as a very promising substrate for in-materio computation at the nanoscale.