We illustrate how emerging methods in artificial intelligence (AI) may be useful in materials science. Historically, these methods were developed in the area of materials process control and, more recently, in the nascent field of materials discovery. However, machine intelligence is of much broader import and our primary objective here is to illustrate how such methods may be used to circumvent some serious roadblocks in the computer simulation of a significant class of computationally hard problems in materials science. This is illustrated by a new approach to solving the dynamics of the N-body problem for large numbers of objects of essentially arbitrarily complex geometry or interaction potential. The approach, based on a particulate artificial neural net dynamics algorithm (PANNDA) is more than two orders of magnitude faster than existing methods when applied to large systems and is only marginally slower (∼10%) than the theoretical lower limiting case of hard spheres. In this method an artificial neural net is trained to predict accurately the time to next collision for binary encounters spanning the Hilbert space of relative positions, orientations and momenta (linear and angular). This approach, which can be extended to soft complex systems, enables construction of exact, albeit numerical, models for the thermodynamic, transport and non-equilibrium properties of very large ensembles of hard or soft objects of arbitrarily complex shape or interaction potential. Our results open up the possibility of immediate application to an usually wide spectrum of contemporary computationally intractable “hard” problems ranging from granular materials with asperities through inclusion of complex many-body terms in the intermolecular interaction in molecular dynamics calculations of complex fluids and polymers.