  Mechanical metamaterials actuators achieve pre-determined input--output
operations exploiting architectural features encoded within a single 3D printed
element, thus removing the need of assembling different structural components.
Despite the rapid progress in the field, there is still a need for efficient
strategies to optimize metamaterial design for a variety of functions. We
present a computational method for the automatic design of mechanical
metamaterial actuators that combines a reinforced Monte Carlo method with
discrete element simulations. 3D printing of selected mechanical metamaterial
actuators shows that the machine-generated structures can reach high
efficiency, exceeding human-designed structures. We also show that it is
possible to design efficient actuators by training a deep neural network,
eliminating the need for lengthy mechanical simulations. The elementary
actuators devised here can be combined to produce metamaterial machines of
arbitrary complexity for countless engineering applications.
