  Additive manufacturing has become one of the forefront technologies in
fabrication, enabling new products impossible to manufacture before. Although
many materials exist for additive manufacturing, they typically suffer from
performance trade-offs preventing them from replacing traditional manufacturing
techniques. Current materials are designed with inefficient human-driven
intuition-based methods, leaving them short of optimal solutions. We propose a
machine learning approach to accelerate the discovery of additive manufacturing
materials with optimal trade-offs in mechanical performance. A multi-objective
optimization algorithm automatically guides the experimental design by
proposing how to mix primary formulations to create better-performing
materials. The algorithm is coupled with a semi-autonomous fabrication platform
to significantly reduce the number of performed experiments and overall time to
solution. Without any prior knowledge of the primary formulations, the proposed
methodology autonomously uncovers twelve optimal composite formulations and
enlarges the discovered performance space 288 times after only 30 experimental
iterations. This methodology could easily be generalized to other material
formulation problems and enable completely automated discovery of a wide
variety of material designs.

Materials Science